Pub Date : 2025-09-08DOI: 10.1109/OJSP.2025.3607278
Metin Calis;Massimo Mischi;Alle-Jan van der Veen;Raj Thilak Rajan;Borbàla Hunyadi
Tensor decomposition methods for signal processing applications are an active area of research. Real data are often low-rank, noisy, and come in a higher-order format. As such, low-rank tensor approximation methods that account for the high-order structure of the data are often used for denoising. One way to represent a tensor in a low-rank form is to decompose the tensor into a set of orthonormal factor matrices and an all-orthogonal core tensor using a higher-order singular value decomposition. Under noisy measurements, the lower bound for recovering the factor matrices and the core tensor is unknown. In this paper, we exploit the well-studied constrained Cramér-Rao bound to calculate a lower bound on the mean squared error of the unbiased estimates of the components of the multilinear singular value decomposition under additive white Gaussian noise, and we validate our approach through simulations.
{"title":"Constrained Cramér-Rao Bound for Higher-Order Singular Value Decomposition","authors":"Metin Calis;Massimo Mischi;Alle-Jan van der Veen;Raj Thilak Rajan;Borbàla Hunyadi","doi":"10.1109/OJSP.2025.3607278","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3607278","url":null,"abstract":"Tensor decomposition methods for signal processing applications are an active area of research. Real data are often low-rank, noisy, and come in a higher-order format. As such, low-rank tensor approximation methods that account for the high-order structure of the data are often used for denoising. One way to represent a tensor in a low-rank form is to decompose the tensor into a set of orthonormal factor matrices and an all-orthogonal core tensor using a higher-order singular value decomposition. Under noisy measurements, the lower bound for recovering the factor matrices and the core tensor is unknown. In this paper, we exploit the well-studied constrained Cramér-Rao bound to calculate a lower bound on the mean squared error of the unbiased estimates of the components of the multilinear singular value decomposition under additive white Gaussian noise, and we validate our approach through simulations.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"1048-1055"},"PeriodicalIF":2.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11153050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-28DOI: 10.1109/OJSP.2025.3604381
Quade Butler;Youssef Ziada;S. Andrew Gadsden
Gaussian filters use quadrature rules or cubature rules to recursively solve Gaussian-weighted integrals. Classical and contemporary methods use stable rules with a minimal number of cubature points to achieve the highest accuracy. Gaussian quadrature is widely believed to be optimal due to its polynomial degree of exactness and higher degree cubature methods often require complex optimization to solve moment equations. In this paper, Gaussian-weighted integrals and Gaussian filtering are approached using a double exponential (DE) transformation and the trapezoidal rule. The DE rule is principled in high rates of convergence for certain integrands and the DE transform ensures that the trapezoidal rule maximizes its performance. A novel spherical-radial cubature rule is derived for Gaussian-weighted integrals where it is shown to be perfectly stable and highly efficient. A new Gaussian filter is then built on top of this cubature rule. The filter is shown to be stable with bounded estimation error. The effect of varying the number of cubature points on filter stability and convergence is also examined. The advantages of the DE method over comparable Gaussian filters and their cubature methods are outlined. These advantages are realized in two numerical examples: a challenging non-polynomial integral and a benchmark filtering problem. The results show that simple and fundamental cubature methods can lead to great improvements in performance when applied correctly.
{"title":"Gaussian Filtering Using a Spherical-Radial Double Exponential Cubature","authors":"Quade Butler;Youssef Ziada;S. Andrew Gadsden","doi":"10.1109/OJSP.2025.3604381","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3604381","url":null,"abstract":"Gaussian filters use quadrature rules or cubature rules to recursively solve Gaussian-weighted integrals. Classical and contemporary methods use stable rules with a minimal number of cubature points to achieve the highest accuracy. Gaussian quadrature is widely believed to be optimal due to its polynomial degree of exactness and higher degree cubature methods often require complex optimization to solve moment equations. In this paper, Gaussian-weighted integrals and Gaussian filtering are approached using a double exponential (DE) transformation and the <italic>trapezoidal rule</i>. The DE rule is principled in high rates of convergence for certain integrands and the DE transform ensures that the trapezoidal rule maximizes its performance. A novel spherical-radial cubature rule is derived for Gaussian-weighted integrals where it is shown to be perfectly stable and highly efficient. A new Gaussian filter is then built on top of this cubature rule. The filter is shown to be stable with bounded estimation error. The effect of varying the number of cubature points on filter stability and convergence is also examined. The advantages of the DE method over comparable Gaussian filters and their cubature methods are outlined. These advantages are realized in two numerical examples: a challenging non-polynomial integral and a benchmark filtering problem. The results show that simple and fundamental cubature methods can lead to great improvements in performance when applied correctly.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"1056-1076"},"PeriodicalIF":2.7,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11144509","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-25DOI: 10.1109/OJSP.2025.3602742
Kazutoshi Akita;Norimichi Ukita
Super-resolution (SR) with arbitrary scale factor and cost-and-quality controllability at test time is essential for various applications. While several arbitrary-scale SR methods have been proposed, these methods require us to modify the model structure and retrain it to control the computational cost and SR quality. To address this limitation, we propose a novel SR method using a Recurrent Neural Network (RNN) with the Fourier representation. In our method, the RNN sequentially estimates Fourier components, each consisting of frequency and amplitude, and aggregates these components to reconstruct an SR image. Since the RNN can adjust the number of recurrences at test time, we can control the computational cost and SR quality in a single model: fewer recurrences (i.e., fewer Fourier components) lead to lower cost but lower quality, while more recurrences (i.e., more Fourier components) lead to better quality but more cost. Experimental results prove that more Fourier components improve the PSNR score. Furthermore, even with fewer Fourier components, our method achieves a lower PSNR drop than other state-of-the-art arbitrary-scale SR methods.
{"title":"Test-Time Cost-and-Quality Controllable Arbitrary-Scale Super-Resolution With Variable Fourier Components","authors":"Kazutoshi Akita;Norimichi Ukita","doi":"10.1109/OJSP.2025.3602742","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3602742","url":null,"abstract":"Super-resolution (SR) with arbitrary scale factor and cost-and-quality controllability at test time is essential for various applications. While several arbitrary-scale SR methods have been proposed, these methods require us to modify the model structure and retrain it to control the computational cost and SR quality. To address this limitation, we propose a novel SR method using a Recurrent Neural Network (RNN) with the Fourier representation. In our method, the RNN sequentially estimates Fourier components, each consisting of frequency and amplitude, and aggregates these components to reconstruct an SR image. Since the RNN can adjust the number of recurrences at test time, we can control the computational cost and SR quality in a single model: fewer recurrences (i.e., fewer Fourier components) lead to lower cost but lower quality, while more recurrences (i.e., more Fourier components) lead to better quality but more cost. Experimental results prove that more Fourier components improve the PSNR score. Furthermore, even with fewer Fourier components, our method achieves a lower PSNR drop than other state-of-the-art arbitrary-scale SR methods.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"1017-1030"},"PeriodicalIF":2.7,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11141341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, various robust algorithms based on hyperbolic cosine and sine functions, such as hyperbolic cosine (HCAF), exponential hyperbolic cosine, joint logarithmic hyperbolic cosine adaptive filter, etc., have been predominantly employed for different aspects of adaptive filtering, including nonlinear-system-identification. Further, in this manuscript, an attempt is made to elevate the performance of nonlinear system identification in the wake of impulsive noise interference along with consideration of a sparse environment. Henceforth, in lieu of this, the present paper introduces a new sparsity-apprised logarithmic hyperbolic tan adaptive filter (SA-LHTAF) to handle impulsive noise while dealing with sparse systems. It utilizes a $l_{1}$ norm-related sparsity penalty factor in the robust cost function constructed with a logarithmic hyperbolic tangent function. Further, an improved SA-LHTAF (ISA-LHTAF) is introduced for varying sparsity or moderately sparse systems employing the log sum penalty factor in the proposed technique. The weight update for the proposed technique has been derived from the modified cost function. In addition, the conditions for the upper bound on the convergence factor have been derived. The efficacy of the developed robust techniques is demonstrated for identifying nonlinear systems along with feedback paths of behind-the-ear (BTE) hearing aid. In addition, the proposed techniques are evaluated for training an acoustic feedback canceller for hearing aids.
{"title":"Sparsity Apprised Logarithmic Hyperbolic Tan Adaptive Filters for Nonlinear System Identification and Acoustic Feedback Cancellation","authors":"Neetu Chikyal;Vasundhara;Chayan Bhar;Asutosh Kar;Mads Græsbøll Christensen","doi":"10.1109/OJSP.2025.3600904","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3600904","url":null,"abstract":"Recently, various robust algorithms based on hyperbolic cosine and sine functions, such as hyperbolic cosine (HCAF), exponential hyperbolic cosine, joint logarithmic hyperbolic cosine adaptive filter, etc., have been predominantly employed for different aspects of adaptive filtering, including nonlinear-system-identification. Further, in this manuscript, an attempt is made to elevate the performance of nonlinear system identification in the wake of impulsive noise interference along with consideration of a sparse environment. Henceforth, in lieu of this, the present paper introduces a new sparsity-apprised logarithmic hyperbolic tan adaptive filter (SA-LHTAF) to handle impulsive noise while dealing with sparse systems. It utilizes a <inline-formula><tex-math>$l_{1}$</tex-math></inline-formula> norm-related sparsity penalty factor in the robust cost function constructed with a logarithmic hyperbolic tangent function. Further, an improved SA-LHTAF (ISA-LHTAF) is introduced for varying sparsity or moderately sparse systems employing the log sum penalty factor in the proposed technique. The weight update for the proposed technique has been derived from the modified cost function. In addition, the conditions for the upper bound on the convergence factor have been derived. The efficacy of the developed robust techniques is demonstrated for identifying nonlinear systems along with feedback paths of behind-the-ear (BTE) hearing aid. In addition, the proposed techniques are evaluated for training an acoustic feedback canceller for hearing aids.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"1031-1047"},"PeriodicalIF":2.7,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11130900","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-11DOI: 10.1109/OJSP.2025.3597873
Phan Thi Huyen Thanh;Trung Thai Tran;The Hiep Nguyen;Minh Huy Vu Nguyen;Tran Vu Pham;Truong Vinh Truong Duy;Duc Dung Nguyen
This paper introduces a general plug-in framework designed to enhance the robustness and cross-domain generalization of self-supervised depth estimation models. Current models often struggle with real-world deployment due to their limited ability to generalize across diverse domains, such as varying lighting and weather conditions. Single-domain models are optimized for specific scenarios while existing multi-domain approaches typically rely on paired images, which are rarely available in real-world datasets. Our framework addresses these limitations by training directly on unpaired real images from multiple domains. Daytime images serve as a reference to guide the model in learning consistent depth distributions across these diverse domains through adversarial training, eliminating the need for paired images. To refine regions prone to artifacts, we augment the discriminator with positional encoding, which is combined with the predicted depth maps. We also incorporate a dynamic normalization mechanism to capture shared depth features across domains, removing the requirement for separate domain-specific encoders. Furthermore, we introduce a new benchmark designed for a more comprehensive evaluation, encompassing previously unaddressed real-world scenarios. By focusing on unpaired real data, our framework significantly improves the generalization capabilities of existing models, enabling them to better adapt to the complexities and authentic data encountered in real-world environments.
{"title":"ULDepth: Transform Self-Supervised Depth Estimation to Unpaired Multi-Domain Learning","authors":"Phan Thi Huyen Thanh;Trung Thai Tran;The Hiep Nguyen;Minh Huy Vu Nguyen;Tran Vu Pham;Truong Vinh Truong Duy;Duc Dung Nguyen","doi":"10.1109/OJSP.2025.3597873","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3597873","url":null,"abstract":"This paper introduces a general plug-in framework designed to enhance the robustness and cross-domain generalization of self-supervised depth estimation models. Current models often struggle with real-world deployment due to their limited ability to generalize across diverse domains, such as varying lighting and weather conditions. Single-domain models are optimized for specific scenarios while existing multi-domain approaches typically rely on paired images, which are rarely available in real-world datasets. Our framework addresses these limitations by training directly on unpaired real images from multiple domains. Daytime images serve as a reference to guide the model in learning consistent depth distributions across these diverse domains through adversarial training, eliminating the need for paired images. To refine regions prone to artifacts, we augment the discriminator with positional encoding, which is combined with the predicted depth maps. We also incorporate a dynamic normalization mechanism to capture shared depth features across domains, removing the requirement for separate domain-specific encoders. Furthermore, we introduce a new benchmark designed for a more comprehensive evaluation, encompassing previously unaddressed real-world scenarios. By focusing on unpaired real data, our framework significantly improves the generalization capabilities of existing models, enabling them to better adapt to the complexities and authentic data encountered in real-world environments.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"1004-1016"},"PeriodicalIF":2.7,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11122640","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144928877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-11DOI: 10.1109/OJSP.2025.3597867
Evan Scope Crafts;Umberto Villa
In recent years, the ascendance of diffusion modeling as a state-of-the-art generative modeling approach has spurred significant interest in their use as priors in Bayesian inverse problems. However, it is unclear how to optimally integrate a diffusion model trained on the prior distribution with a given likelihood function to obtain posterior samples. While algorithms developed for this purpose can produce high-quality, diverse point estimates of the unknown parameters of interest, they are often tested on problems where the prior distribution is analytically unknown, making it difficult to assess their performance in providing rigorous uncertainty quantification. Motivated by this challenge, this work introduces three benchmark problems for evaluating the performance of diffusion model based samplers. The benchmark problems, which are inspired by problems in image inpainting, x-ray tomography, and phase retrieval, have a posterior density that is analytically known. In this setting, approximate ground-truth posterior samples can be obtained, enabling principled evaluation of the performance of posterior sampling algorithms. This work also introduces a general framework for diffusion model based posterior sampling, Bayesian Inverse Problem Solvers through Diffusion Annealing (BIPSDA). This framework unifies several recently proposed diffusion-model-based posterior sampling algorithms and contains novel algorithms that can be realized through flexible combinations of design choices. We tested the performance of a set of BIPSDA algorithms, including previously proposed state-of-the-art approaches, on the proposed benchmark problems. The results provide insight into the strengths and limitations of existing diffusion-model based posterior samplers, while the benchmark problems provide a testing ground for future algorithmic developments.
{"title":"Benchmarking Diffusion Annealing-Based Bayesian Inverse Problem Solvers","authors":"Evan Scope Crafts;Umberto Villa","doi":"10.1109/OJSP.2025.3597867","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3597867","url":null,"abstract":"In recent years, the ascendance of diffusion modeling as a state-of-the-art generative modeling approach has spurred significant interest in their use as priors in Bayesian inverse problems. However, it is unclear how to optimally integrate a diffusion model trained on the prior distribution with a given likelihood function to obtain posterior samples. While algorithms developed for this purpose can produce high-quality, diverse point estimates of the unknown parameters of interest, they are often tested on problems where the prior distribution is analytically unknown, making it difficult to assess their performance in providing rigorous uncertainty quantification. Motivated by this challenge, this work introduces three benchmark problems for evaluating the performance of diffusion model based samplers. The benchmark problems, which are inspired by problems in image inpainting, x-ray tomography, and phase retrieval, have a posterior density that is analytically known. In this setting, approximate ground-truth posterior samples can be obtained, enabling principled evaluation of the performance of posterior sampling algorithms. This work also introduces a general framework for diffusion model based posterior sampling, Bayesian Inverse Problem Solvers through Diffusion Annealing (BIPSDA). This framework unifies several recently proposed diffusion-model-based posterior sampling algorithms and contains novel algorithms that can be realized through flexible combinations of design choices. We tested the performance of a set of BIPSDA algorithms, including previously proposed state-of-the-art approaches, on the proposed benchmark problems. The results provide insight into the strengths and limitations of existing diffusion-model based posterior samplers, while the benchmark problems provide a testing ground for future algorithmic developments.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"975-991"},"PeriodicalIF":2.7,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11122619","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Low-complexity channel estimation techniques are key to enabling efficient, reliable, and real-time communication in modern wireless devices operating under resource and energy constraints. This paper presents for the first time a low-complexity peak-power-assisted data-aided channel estimation (DACE) scheme for both single-input single-output (SISO) and multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless systems. In OFDM, high peak-power levels occur when the subcarriers align in phase and constructively interfere with each other. The research proposes a peak-power-assisted channel estimation scheme that accurately selects peak-power carriers at the transmitter of an OFDM system and uses them as reliable carriers for the DACE scheme. By incorporating these reliable carriers with known pilot symbols as additional pilot signals, channel estimation accuracy significantly improves in MIMO-OFDM systems. This eliminates the need to determine reliable data symbols at the receiver, thereby significantly reducing the computational complexity of the system. However, high peak-powers are considered a major drawback in OFDM. In this work, we incorporate a companding technique to mitigate this issue and provide sufficient margin for the DACE scheme. The performance of the proposed DACE scheme is evaluated using both least square (LS) and linear minimum mean square error (LMMSE) channel estimators. In this regard, the proposed technique not only improves channel estimation accuracy but also enhances the spectral efficiency of the wireless system. It outperforms traditional channel estimators in terms of system mean square error (MSE) and bit-error-rate (BER) performance. It also reduces the pilot overhead by 50$%$ compared to traditional channel estimators and provides bandwidth optimization for MIMO-OFDM systems. This makes it a promising solution for enhancing the performance and efficiency of next-generation wireless communication systems across diverse applications.
{"title":"A Novel Low-Complexity Peak-Power-Assisted Data-Aided Channel Estimation Scheme for MIMO-OFDM Wireless Systems","authors":"Inaamullah Khan;Mohammad Mahmudul Hasan;Michael Cheffena","doi":"10.1109/OJSP.2025.3595039","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3595039","url":null,"abstract":"Low-complexity channel estimation techniques are key to enabling efficient, reliable, and real-time communication in modern wireless devices operating under resource and energy constraints. This paper presents for the first time a low-complexity peak-power-assisted data-aided channel estimation (DACE) scheme for both single-input single-output (SISO) and multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless systems. In OFDM, high peak-power levels occur when the subcarriers align in phase and constructively interfere with each other. The research proposes a peak-power-assisted channel estimation scheme that accurately selects peak-power carriers at the transmitter of an OFDM system and uses them as reliable carriers for the DACE scheme. By incorporating these reliable carriers with known pilot symbols as additional pilot signals, channel estimation accuracy significantly improves in MIMO-OFDM systems. This eliminates the need to determine reliable data symbols at the receiver, thereby significantly reducing the computational complexity of the system. However, high peak-powers are considered a major drawback in OFDM. In this work, we incorporate a companding technique to mitigate this issue and provide sufficient margin for the DACE scheme. The performance of the proposed DACE scheme is evaluated using both least square (LS) and linear minimum mean square error (LMMSE) channel estimators. In this regard, the proposed technique not only improves channel estimation accuracy but also enhances the spectral efficiency of the wireless system. It outperforms traditional channel estimators in terms of system mean square error (MSE) and bit-error-rate (BER) performance. It also reduces the pilot overhead by 50<inline-formula><tex-math>$%$</tex-math></inline-formula> compared to traditional channel estimators and provides bandwidth optimization for MIMO-OFDM systems. This makes it a promising solution for enhancing the performance and efficiency of next-generation wireless communication systems across diverse applications.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"992-1003"},"PeriodicalIF":2.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11106695","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1109/OJSP.2025.3595046
Exequiel Oliva;Nelson Díaz;Samuel Pinilla;Esteban Vera
Extended depth-of-field (EDoF) is a desirable attribute for imaging systems where all features in the scene are in focus despite their relative distance. Traditional imaging systems can achieve EDoF by reducing the aperture size at the expense of signal-to-noise ratio, particularly relevant in spectral imaging systems where incoming light is further divided. By designing and integrating diffractive optical elements (DOEs) placed at the aperture plane of the imaging system, wavefront coding has enabled EDoF while maintaining a larger aperture size at the expense of post-processing. Nevertheless, chromatic aberrations may appear and can often be confused by defocus, jeopardizing the fidelity of the reconstructions. This work presents a novel design approach for a multispectral-aware DOE for EDoF. By considering and modeling a refractive-diffractive optical setup, our proposed system uses the stochastic optimization framework to optimize DOE patterns to preserve spectral fidelity while extending the depth-of-field simultaneously. The optimization process exploits the covariance matrix adaptation evolution strategy (CMA-ES), efficiently exploring complex, high-dimensional phase configurations without the need for explicit gradient information. The optimized DOE is constantly evaluated in a simulated imaging pipeline where the EDoF multispectral datacube is deblurred using Richardson-Lucy deconvolution. Both qualitative and quantitative results demonstrate that the proposed DOE significantly improves depth invariance and spectral fidelity of the reconstructed datacubes compared to conventional and state-of-the-art DOE designs, making it a cost-effective solution for real-world multispectral EDoF applications.
{"title":"Multispectral Extended Depth-of-Field Imaging via Stochastic Wavefront Optimization","authors":"Exequiel Oliva;Nelson Díaz;Samuel Pinilla;Esteban Vera","doi":"10.1109/OJSP.2025.3595046","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3595046","url":null,"abstract":"Extended depth-of-field (EDoF) is a desirable attribute for imaging systems where all features in the scene are in focus despite their relative distance. Traditional imaging systems can achieve EDoF by reducing the aperture size at the expense of signal-to-noise ratio, particularly relevant in spectral imaging systems where incoming light is further divided. By designing and integrating diffractive optical elements (DOEs) placed at the aperture plane of the imaging system, wavefront coding has enabled EDoF while maintaining a larger aperture size at the expense of post-processing. Nevertheless, chromatic aberrations may appear and can often be confused by defocus, jeopardizing the fidelity of the reconstructions. This work presents a novel design approach for a multispectral-aware DOE for EDoF. By considering and modeling a refractive-diffractive optical setup, our proposed system uses the stochastic optimization framework to optimize DOE patterns to preserve spectral fidelity while extending the depth-of-field simultaneously. The optimization process exploits the covariance matrix adaptation evolution strategy (CMA-ES), efficiently exploring complex, high-dimensional phase configurations without the need for explicit gradient information. The optimized DOE is constantly evaluated in a simulated imaging pipeline where the EDoF multispectral datacube is deblurred using Richardson-Lucy deconvolution. Both qualitative and quantitative results demonstrate that the proposed DOE significantly improves depth invariance and spectral fidelity of the reconstructed datacubes compared to conventional and state-of-the-art DOE designs, making it a cost-effective solution for real-world multispectral EDoF applications.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"965-974"},"PeriodicalIF":2.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11106763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-23DOI: 10.1109/OJSP.2025.3589747
Yatao Liu;Mingjie Shao;Wing-Kin Ma
In massive multiple-input multiple-output (MIMO) downlink systems, the physical implementation of the base stations (BSs) requires the use of cheap and power-efficient power amplifiers (PAs) to avoid high hardware cost and high power consumption. However, such PAs usually have limited linear amplification ranges. Nonlinear distortions arising from operation beyond the linear amplification ranges can significantly degrade system performance. Existing approaches to handle the nonlinear distortions, such as digital predistortion (DPD), typically require accurate knowledge, or acquisition, of the PA transfer function. In this paper, we present a new concept for mitigation of the PA distortions. Assuming a uniform linear array (ULA) at the BS, the idea is to apply a Sigma-Delta ($Sigma Delta$) modulator to spatially shape the PA distortions to the high-angle region. By having the system operating in the low-angle region, the received signals are less affected by the PA distortions. To demonstrate the potential of this spatial $Sigma Delta$ approach, we study the application of our approach to the multi-user MIMO-orthogonal frequency division modulation (OFDM) downlink scenario. A symbol-level precoding (SLP) scheme and a zero-forcing (ZF) precoding scheme, with the new design requirement by the spatial $Sigma Delta$ approach being taken into account, are developed. Numerical simulations are performed to show the effectiveness of the developed $Sigma Delta$ precoding schemes.
{"title":"A Spatial Sigma-Delta Approach to Mitigation of Power Amplifier Distortions in Massive MIMO Downlink","authors":"Yatao Liu;Mingjie Shao;Wing-Kin Ma","doi":"10.1109/OJSP.2025.3589747","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3589747","url":null,"abstract":"In massive multiple-input multiple-output (MIMO) downlink systems, the physical implementation of the base stations (BSs) requires the use of cheap and power-efficient power amplifiers (PAs) to avoid high hardware cost and high power consumption. However, such PAs usually have limited linear amplification ranges. Nonlinear distortions arising from operation beyond the linear amplification ranges can significantly degrade system performance. Existing approaches to handle the nonlinear distortions, such as digital predistortion (DPD), typically require accurate knowledge, or acquisition, of the PA transfer function. In this paper, we present a new concept for mitigation of the PA distortions. Assuming a uniform linear array (ULA) at the BS, the idea is to apply a Sigma-Delta (<inline-formula><tex-math>$Sigma Delta$</tex-math></inline-formula>) modulator to spatially shape the PA distortions to the high-angle region. By having the system operating in the low-angle region, the received signals are less affected by the PA distortions. To demonstrate the potential of this spatial <inline-formula><tex-math>$Sigma Delta$</tex-math></inline-formula> approach, we study the application of our approach to the multi-user MIMO-orthogonal frequency division modulation (OFDM) downlink scenario. A symbol-level precoding (SLP) scheme and a zero-forcing (ZF) precoding scheme, with the new design requirement by the spatial <inline-formula><tex-math>$Sigma Delta$</tex-math></inline-formula> approach being taken into account, are developed. Numerical simulations are performed to show the effectiveness of the developed <inline-formula><tex-math>$Sigma Delta$</tex-math></inline-formula> precoding schemes.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"900-916"},"PeriodicalIF":2.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11091482","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1109/OJSP.2025.3590248
Jiale Zhao;Dingding Yao;Junfeng Li
Head-Related Transfer Functions (HRTFs) play a vital role in binaural spatial audio rendering. With the release of numerous HRTF datasets in recent years, abundant data has become available to support HRTF-related research based on deep learning. However, measurement discrepancies across different datasets introduce significant variations in the data and directly merging these datasets may lead to systematic biases. The recent Listener Acoustic Personalization Challenge 2024 (European Signal Processing Conference) dealt with this issue, with the task of harmonizing different datasets to achieve lower classification accuracy while meeting thresholds over various localization metrics. To mitigate cross-dataset differences, this paper proposes a neural network-based HRTF harmonization approach aimed at eliminating dataset-specific properties embedded in the original measurements. The proposed method utilizes a perceptually relevant loss function, which jointly constrains multiple objectives, including interaural level differences, auditory-filter excitation patterns, and classification accuracy. Experimental results based on eight datasets demonstrate that the proposed approach can effectively minimize distributional disparities between datasets while mostly preserving localization performance. The classification accuracy for harmonized HRTFs between different datasets is reduced to as low as 31%, indicating a significant reduction in cross-dataset discrepancies. The proposed method ranked first in this challenge, which validates its effectiveness.
{"title":"Cross-Dataset Head-Related Transfer Function Harmonization Based on Perceptually Relevant Loss Function","authors":"Jiale Zhao;Dingding Yao;Junfeng Li","doi":"10.1109/OJSP.2025.3590248","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3590248","url":null,"abstract":"Head-Related Transfer Functions (HRTFs) play a vital role in binaural spatial audio rendering. With the release of numerous HRTF datasets in recent years, abundant data has become available to support HRTF-related research based on deep learning. However, measurement discrepancies across different datasets introduce significant variations in the data and directly merging these datasets may lead to systematic biases. The recent Listener Acoustic Personalization Challenge 2024 (European Signal Processing Conference) dealt with this issue, with the task of harmonizing different datasets to achieve lower classification accuracy while meeting thresholds over various localization metrics. To mitigate cross-dataset differences, this paper proposes a neural network-based HRTF harmonization approach aimed at eliminating dataset-specific properties embedded in the original measurements. The proposed method utilizes a perceptually relevant loss function, which jointly constrains multiple objectives, including interaural level differences, auditory-filter excitation patterns, and classification accuracy. Experimental results based on eight datasets demonstrate that the proposed approach can effectively minimize distributional disparities between datasets while mostly preserving localization performance. The classification accuracy for harmonized HRTFs between different datasets is reduced to as low as 31%, indicating a significant reduction in cross-dataset discrepancies. The proposed method ranked first in this challenge, which validates its effectiveness.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"865-875"},"PeriodicalIF":2.7,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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