Signal Processing最新文献

In video snapshot compressive imaging (SCI) systems, video reconstruction methods are used to recover spatial–temporal-correlated video frame signals from a compressed measurement. While unfolding methods have demonstrated promising performance, they encounter two challenges: (1) They lack the ability to estimate degradation patterns and the degree of ill-posedness from video SCI, which hampers guiding and supervising the iterative learning process. (2) The prevailing reliance on 3D-CNNs in these methods limits their capacity to capture long-range dependencies. To address these concerns, this paper introduces the Degradation-Aware Deep Unfolding Network (DADUN). DADUN leverages estimated priors from compressed frames and the physical mask to guide and control each iteration. We also develop a novel Bidirectional Propagation Convolutional Recurrent Neural Network (BiP-CRNN) that simultaneously captures both intra-frame contents and inter-frame dependencies. By plugging BiP-CRNN into DADUN, we establish a novel end-to-end (E2E) and data-dependent deep unfolding method, DADUN with transformer prior (TP), for video sequence reconstruction. Experimental results on various video sequences show the effectiveness of our proposed approach, which is also robust to random masks and has wide generalization bounds.

In this paper, we propose a novel energy-focused slow-time MIMO radar and signal processing scheme, aimed at addressing key challenges in slow-time coding and signal processing technology. Conventional slow-time MIMO radar faces issues such as energy waste due to the omnidirectional transmit beampattern of orthogonal coding and the velocity ambiguity problem. To overcome these limitations, the proposed radar system utilizes a method based on Doppler frequency offset diversity (DFOD) for slow-time coding design. This method enables the adjustment of Doppler offset parameters to achieve a rectangular transmit beampattern with any mainlobe width within a single coherent processing interval (CPI), offering the advantage of low computational complexity. Through an analysis of the ambiguity function for DFOD-based coding, we evaluate both Doppler and angular resolution. To further improve Doppler frequency resolution, a slow-time coding design is introduced based on Pulse Random Permutation (PRP). Subsequently, a signal processing scheme based on matched filtering is presented. To tackle the high Doppler sidelobe issue associated with PRP-based coding, we propose a mismatch filter (MMF) design method utilizing convex optimization. Ultimately, the performance enhancement of the proposed slow-time MIMO radar is verified through simulation analysis in comparison to existing technologies.

The sample complexity of a sparse linear model where samples are dependent is studied in this paper. We consider a specific dependency structure of the samples which arises in some experimental designs such as drug sensitivity studies, where two sets of objects (drugs and cells) are sampled independently, and after crossing (making all possible combinations of drugs and cells), the resulting output (efficacy of drugs) is measured. We call these types of samples as “cross samples”. The dependency among such samples is strong, and existing theoretical studies are either inapplicable or fail to provide realistic bounds. We aim at analyzing the performance of the Lasso estimator where the underlying distributions are mixtures of Gaussians and the data dependency arises from the crossing procedure. Our theoretical results show that the performance of the Lasso estimator in case of cross samples follows that of the i.i.d. samples with differences in constant factors. Through numerical results, we observe a phase transition: When datasets are too small, the error for cross samples is much larger than for i.i.d. samples, but once the size is large enough, cross samples are nearly as useful as i.i.d. samples. Our theoretical analysis suggests that the transition threshold is governed by the level of sparsity of the true parameter vector being estimated.

Image inpainting aims to recover damaged regions of a corrupted image and maintain the integrity of the structure and texture within the filled regions. Previous popular approaches have restored images with both vivid textures and structures by introducing structure priors. However, the structure prior-based approaches meet the following main challenges: (1) the fine-grained textures suffer from adverse inpainting effects because they do not fully consider the interaction between structures and textures, (2) the features of the multi-scale objects in structural and textural information cannot be extracted correctly due to the limited receptive fields in convolution operation. In this paper, we propose a texture and structure bidirectional generation network (TSBGNet) to address the above issues. We first reconstruct the texture and structure of corrupted images; then, we design a texture-enhanced-FCMSPCNN (TE-FCMSPCNN) to optimize the generated textures. We also conjoin a bidirectional information flow (BIF) module and a detail enhancement (DE) module to integrate texture and structure features globally. Additionally, we derive a multi-scale attentional feature fusion (MAFF) module to fuse multi-scale features. Experimental results demonstrate that TSBGNet effectively reconstructs realistic contents and significantly outperforms other state-of-the-art approaches on three popular datasets. Moreover, the proposed approach yields promising results on the Dunhuang Mogao Grottoes Mural dataset.

Jittery image is visually abnormal in jags of edge and loss of coherence. The problem of image dejittering is challenging to resolve due to the ubiquitous blur and/or noise in jittery data. In this paper, we devote to the pixel-jitter (possibly blurry) image recovery on the perspective of spatially-varying mixed noise removal. By viewing jittery image as the corruption of ideal image with outliers and spatially-varying Gaussian noise, we proposed a two-phase (including filtering and diffusing phases) image dejittering approach. In the filtering phase, outliers posed by jitters around edges are inspected by median filters. In the diffusing phase, structure tensor based anisotropic diffusion is exploited to reduce the perturbations in piecewise smooth image regions. Upon the spectral decomposition of structure tensor, the variational model in diffusing phase can be solved by some state-of-the-art optimization methods. Numerical simulations on synthetic and real jittery data demonstrate the compelling performance of the proposed approach. The Matlab source codes of the proposed approach are available at the repositories of https://github.com/WenxingZhang.

In recent years, convolutional neural networks and vision transformers have emerged as predominant models for hyperspectral remote sensing image classification task, leveraging staked convolution layers and self-attention mechanisms with high computation costs, respectively. Recent studies, such as the Mamba model, have showcased the ability of state space model (SSM) with efficient hardware-aware designs in efficiently modeling sequences and extracting implicit features along tokens, which is precisely needed for accurate hyperspectral image (HSI) classification. Thus making SSM-based model potentially a new architecture for remote sensing HSI classification task. However, SSM encounters challenges in modeling HSI due to the insensitivity of spatial information and redundant spectral characteristics. Given SSM-based methods rarely explored in HSI classification, in this work, we present the first exploration of SSM-based models for HSI classification task. Our proposed method MamTrans effectively leverages the capacity of transformer for capturing spatial tokens relationships and Mamba for extracting implicit features along tokens. Besides, we propose a Bidirectional Mamba Module to enhance SSM’s spatial perception ability of extracting spatial features in HSI. Our proposed MamTrans obtains a new state-of-the-art performance across five commonly employed HSI classification benchmarks, demonstrating the robust generalization of MamTrans and effectiveness of SSM-based structure for HSI classification task. Our codes could be found at https://github.com/PPPPPsanG/MamTrans.

With an increasing influx of classical signal processing methodologies into the field of graph signal processing, approaches grounded in discrete linear canonical transform have found application in graph signals. In this paper, we initially propose the uncertainty principle of the graph linear canonical transform (GLCT), which is based on a class of graph signals maximally concentrated in both vertex and graph spectral domains. Subsequently, leveraging the uncertainty principle, we establish conditions for recovering bandlimited signals of the GLCT from a subset of samples, thereby formulating the sampling theory for the GLCT. We elucidate interesting connections between the uncertainty principle and sampling. Further, by employing sampling set selection and experimental design sampling strategies, we introduce optimal sampling operators in the GLCT domain. Finally, we evaluate the performance of our methods through simulations and numerical experiments across applications.

This paper proposes a sparsity-inducing regularizer associated with the Welsch function. We theoretically show that the regularizer is quasiconvex and the corresponding Moreau envelope is convex. Moreover, the closed-form solution to its Moreau envelope, namely, the proximity operator, is derived. Unlike conventional nonconvex regularizers like the ${\ell }_p$-norm with $0<p<1$ that generally needs iterations to obtain the corresponding proximity operator, the developed regularizer has a closed-form proximity operator. We utilize our regularizer to penalize the singular values as well as sparse outliers of the distorted data, and develop an efficient algorithm for robust matrix completion. Convergence of the suggested method is analyzed and we prove that any accumulation point is a stationary point. Finally, experimental results demonstrate that our algorithm is superior to the competing techniques in terms of restoration performance. MATALB codes are available at https://github.com/bestzywang/RMC-NNSR.

Discriminative dictionary learning (DDL) has been confirmed to be effective for image classification. However, existing DDL approaches often fail to extract deep hierarchical information due to the single-layer dictionary learning framework. Moreover, they overlook the atoms-label information in the dictionary, leading to reduced feature distinctiveness and lower classification accuracy. To overcome the above problem, a novel DDL method, called the Multi-layer local constraint and label embedding dictionary learning (M-LCLEDL), is proposed. Specifically, the novel multi-layer DDL framework, which is formed by stacking the DL process one by one, is designed to learn the deep hierarchical and nonlinear features. The layer-by-layer stacking of the DL process in the multi-layer DDL framework allows for the elimination of redundant and interfering features. This step-by-step elimination process enhances the stability and robustness of the framework. Additionally, to leverage the label information carried by the labeled training samples, atoms with label embedding and locality structure are introduced. The proposed approach includes a fast iteration strategy for efficient optimization. Experimental results demonstrate that the approach is relatively insensitive to dictionary size, achieving promising performance and greater stability compared to most DDL-based image classification approaches.

Driven terms in active contour models (ACMs) play a significant role in edge identification and image segmentation. However, in many existing ACMs, the driven terms are iteratively updated, resulting in slower segmentation speed because the image segmentation time increases with the iteration number. To address this problem, an ACM based on an improved second-order differential driven term (ISDDT) is presented, which can extract the edge information of images. The improved second-order differential driven term is computed only once before the iterations. Therefore, the computational complexity of our presented ACM is reduced, leading to a faster image segmentation speed. In addition, an improved regularization method with mean filtering is presented to improve the robustness of our ISDDT model. As an application, a target contour tracking method is developed based on our ISDDT model. Experimental results show that our ISDDT model segments images with inhomogeneous intensities well. The image segmentation speed of our proposed model has obvious advantages.