Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9191130
June Hao Ching, John See, L. Wong
Due to the high capability of learning robust features, convolutional neural networks (CNN) are becoming a mainstay solution for many computer vision problems, including aesthetic quality assessment (AQA). However, there remains the issue that learning with CNN requires time-consuming and expensive data annotations especially for a task like AQA. In this paper, we present a novel approach to AQA that incorporates self-supervised learning (SSL) by learning how to inpaint images according to photographic rules such as rules-of-thirds and visual saliency. We conduct extensive quantitative experiments on a variety of pretext tasks and also different ways of masking patches for inpainting, reporting fairer distribution-based metrics. We also show the suitability and practicality of the inpainting task which yielded comparably good benchmark results with much lighter model complexity.
{"title":"Learning Image Aesthetics by Learning Inpainting","authors":"June Hao Ching, John See, L. Wong","doi":"10.1109/ICIP40778.2020.9191130","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9191130","url":null,"abstract":"Due to the high capability of learning robust features, convolutional neural networks (CNN) are becoming a mainstay solution for many computer vision problems, including aesthetic quality assessment (AQA). However, there remains the issue that learning with CNN requires time-consuming and expensive data annotations especially for a task like AQA. In this paper, we present a novel approach to AQA that incorporates self-supervised learning (SSL) by learning how to inpaint images according to photographic rules such as rules-of-thirds and visual saliency. We conduct extensive quantitative experiments on a variety of pretext tasks and also different ways of masking patches for inpainting, reporting fairer distribution-based metrics. We also show the suitability and practicality of the inpainting task which yielded comparably good benchmark results with much lighter model complexity.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122094282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9191297
Jack Erdozain, Kazuto Ichimaru, Tomohiro Maeda, Hiroshi Kawasaki, R. Raskar, A. Kadambi
Optical 3D sensing technologies are exploited for many applications in autonomous vehicles, manufacturing, and consumer products. However, existing techniques may suffer in certain challenging conditions, where scattering may occur due to particles. While the light in the visible and near IR spectrum is affected by scattering, long-wave IR (LWIR) tends to experience less scattering, especially when the particles are much smaller than the incident radiation. We propose and demonstrate the expansion of structured light scanning approaches into the LWIR spectrum using a thermal camera and black body radiation source. We then validate the results produced against ground truth scans from traditional structured light scanners. Additional means for projecting these scanning patterns are also discussed alongside potential drawbacks and challenges of this technique associated with future adoption.
{"title":"3d Imaging For Thermal Cameras Using Structured Light","authors":"Jack Erdozain, Kazuto Ichimaru, Tomohiro Maeda, Hiroshi Kawasaki, R. Raskar, A. Kadambi","doi":"10.1109/ICIP40778.2020.9191297","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9191297","url":null,"abstract":"Optical 3D sensing technologies are exploited for many applications in autonomous vehicles, manufacturing, and consumer products. However, existing techniques may suffer in certain challenging conditions, where scattering may occur due to particles. While the light in the visible and near IR spectrum is affected by scattering, long-wave IR (LWIR) tends to experience less scattering, especially when the particles are much smaller than the incident radiation. We propose and demonstrate the expansion of structured light scanning approaches into the LWIR spectrum using a thermal camera and black body radiation source. We then validate the results produced against ground truth scans from traditional structured light scanners. Additional means for projecting these scanning patterns are also discussed alongside potential drawbacks and challenges of this technique associated with future adoption.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122166757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9190730
Sk Aziz Ali, Sikang Yan, W. Dornisch, D. Stricker
In this paper, we propose a new template fitting method that can capture fine details of garments in target 3D scans of dressed human bodies. Matching the high fidelity details of such loose/tight-fit garments is a challenging task as they express intricate folds, creases, wrinkle patterns, and other high fidelity surface details. Our proposed method of non-rigid shape fitting – FoldMatch – uses physics-based particle dynamics to explicitly model the deformation of loose-fit garments and wrinkle vector fields for capturing clothing details. The 3D scan point cloud behaves as a collection of astrophysical particles, which attracts the points in template mesh and defines the template motion model. We use this point-based motion model to derive regularized deformation gradients for the template mesh. We show the parameterization of the wrinkle vector fields helps in the accurate shape fitting. Our method shows better performance than the stateof-the-art methods. We define several deformation and shape matching quality measurement metrics to evaluate FoldMatch on synthetic and real data sets.
{"title":"Foldmatch: Accurate and High Fidelity Garment Fitting Onto 3D Scans","authors":"Sk Aziz Ali, Sikang Yan, W. Dornisch, D. Stricker","doi":"10.1109/ICIP40778.2020.9190730","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9190730","url":null,"abstract":"In this paper, we propose a new template fitting method that can capture fine details of garments in target 3D scans of dressed human bodies. Matching the high fidelity details of such loose/tight-fit garments is a challenging task as they express intricate folds, creases, wrinkle patterns, and other high fidelity surface details. Our proposed method of non-rigid shape fitting – FoldMatch – uses physics-based particle dynamics to explicitly model the deformation of loose-fit garments and wrinkle vector fields for capturing clothing details. The 3D scan point cloud behaves as a collection of astrophysical particles, which attracts the points in template mesh and defines the template motion model. We use this point-based motion model to derive regularized deformation gradients for the template mesh. We show the parameterization of the wrinkle vector fields helps in the accurate shape fitting. Our method shows better performance than the stateof-the-art methods. We define several deformation and shape matching quality measurement metrics to evaluate FoldMatch on synthetic and real data sets.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116612663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9190686
P. Pokala, Satvik Chemudupati, C. Seelamantula
We present a new method for fast magnetic resonance image (MRI) reconstruction in the complex-domain under tight frames. We propose a generalized problem formulation that allows for different weight-update strategies for iteratively reweighted ℓ1-minimization under tight frames. Further, we impose sufficient conditions on the function of the weights that leads to the reweighting strategy, which follows the interpretation originally given by Candès et al, but is more efficient than theirs. Since the objective function in complex-domain compressive sensing MRI (CS-MRI) reconstruction problem is nonholomorphic, we resort to Wirtinger calculus for deriving the update strategies. We develop an algorithm called generalized iteratively reweighted soft-thresholding algorithm (GIRSTA) and its fast variant, namely, generalized fast iteratively reweighted soft-thresholding algorithm (GFIRSTA). We provide convergence guarantees for GIRSTA and empirical convergence results for GFIRSTA. Our experiments show a remarkable performance of the proposed algorithms for complex-domain CS-MRI reconstruction considering both random sampling and radial sampling strategies. GFIRSTA outperforms state-of-the-art techniques in terms of peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM).
{"title":"Generalized Fast Iteratively Reweighted Soft-Thresholding Algorithm for Sparse Coding Under Tight Frames in the Complex-Domain","authors":"P. Pokala, Satvik Chemudupati, C. Seelamantula","doi":"10.1109/ICIP40778.2020.9190686","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9190686","url":null,"abstract":"We present a new method for fast magnetic resonance image (MRI) reconstruction in the complex-domain under tight frames. We propose a generalized problem formulation that allows for different weight-update strategies for iteratively reweighted ℓ1-minimization under tight frames. Further, we impose sufficient conditions on the function of the weights that leads to the reweighting strategy, which follows the interpretation originally given by Candès et al, but is more efficient than theirs. Since the objective function in complex-domain compressive sensing MRI (CS-MRI) reconstruction problem is nonholomorphic, we resort to Wirtinger calculus for deriving the update strategies. We develop an algorithm called generalized iteratively reweighted soft-thresholding algorithm (GIRSTA) and its fast variant, namely, generalized fast iteratively reweighted soft-thresholding algorithm (GFIRSTA). We provide convergence guarantees for GIRSTA and empirical convergence results for GFIRSTA. Our experiments show a remarkable performance of the proposed algorithms for complex-domain CS-MRI reconstruction considering both random sampling and radial sampling strategies. GFIRSTA outperforms state-of-the-art techniques in terms of peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM).","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129712268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9191027
Jiawen Liao, C. Qi, Jianzhong Cao, He Bian
Classical discriminative correlation filter (DCF) model suffers from boundary effects, several modified discriminative correlation filter models have been proposed to mitigate this drawback using enlarged search region, and remarkable performance improvement has been reported by related papers. However, model deterioration is still not well addressed when facing occlusion and other challenging scenarios. In this work, we propose a novel Temporally-regularized Context-aware Correlation Filters (TCCF) model to model the target appearance more robustly. We take advantage of the enlarged search region to obtain more negative samples to make the filter sufficiently trained, and a temporal regularizer, which restricting variation in filter models between frames, is seamlessly integrated into the original formulation. Our model is derived from the new discriminative learning loss formulation, a closed form solution for multidimensional features is provided, which is solved efficiently using Alternating Direction Method of Multipliers (ADMM). Extensive experiments on standard OTB-2015, TempleColor-128 and VOT-2016 benchmarks show that the proposed approach performs favorably against many state-of-the-art methods with real-time performance of 28fps on single CPU.
{"title":"Visual Tracking Via Temporally-Regularized Context-Aware Correlation Filters","authors":"Jiawen Liao, C. Qi, Jianzhong Cao, He Bian","doi":"10.1109/ICIP40778.2020.9191027","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9191027","url":null,"abstract":"Classical discriminative correlation filter (DCF) model suffers from boundary effects, several modified discriminative correlation filter models have been proposed to mitigate this drawback using enlarged search region, and remarkable performance improvement has been reported by related papers. However, model deterioration is still not well addressed when facing occlusion and other challenging scenarios. In this work, we propose a novel Temporally-regularized Context-aware Correlation Filters (TCCF) model to model the target appearance more robustly. We take advantage of the enlarged search region to obtain more negative samples to make the filter sufficiently trained, and a temporal regularizer, which restricting variation in filter models between frames, is seamlessly integrated into the original formulation. Our model is derived from the new discriminative learning loss formulation, a closed form solution for multidimensional features is provided, which is solved efficiently using Alternating Direction Method of Multipliers (ADMM). Extensive experiments on standard OTB-2015, TempleColor-128 and VOT-2016 benchmarks show that the proposed approach performs favorably against many state-of-the-art methods with real-time performance of 28fps on single CPU.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129859273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9190996
Junyong You, J. Korhonen
Video classification can be performed by summarizing image contents of individual frames into one class by deep neural networks, e.g., CNN and LSTM. Human interpretation of video content is influenced by the attention mechanism. In other words, video class can be more attentively decided by certain information than others. In this paper, we propose to integrate the attention mechanism into deep networks for video classification. The proposed framework employs 2D CNN networks with ImageNet pretrained weights to extract features of video frames that are then fed to a bidirectional LSTM network for video classification. An attention block has been developed that can be added after the LSTM network in the proposed framework. Several different 2D CNN architectures have been tested in the experiments. The results with respect to two publicly available datasets have demonstrated that integrating attention can boost the performance of deep networks in video classification compared to not applying the attention block. We also found out that applying attention to the LSTM outputs on the VGG19 architecture provides the highest classification accuracy in the proposed framework.
{"title":"Attention Boosted Deep Networks For Video Classification","authors":"Junyong You, J. Korhonen","doi":"10.1109/ICIP40778.2020.9190996","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9190996","url":null,"abstract":"Video classification can be performed by summarizing image contents of individual frames into one class by deep neural networks, e.g., CNN and LSTM. Human interpretation of video content is influenced by the attention mechanism. In other words, video class can be more attentively decided by certain information than others. In this paper, we propose to integrate the attention mechanism into deep networks for video classification. The proposed framework employs 2D CNN networks with ImageNet pretrained weights to extract features of video frames that are then fed to a bidirectional LSTM network for video classification. An attention block has been developed that can be added after the LSTM network in the proposed framework. Several different 2D CNN architectures have been tested in the experiments. The results with respect to two publicly available datasets have demonstrated that integrating attention can boost the performance of deep networks in video classification compared to not applying the attention block. We also found out that applying attention to the LSTM outputs on the VGG19 architecture provides the highest classification accuracy in the proposed framework.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128380269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9190754
M. Ghazal, Ali M. Mahmoud, A. Shalaby, Shams Shaker, A. Khelifi, A. El-Baz
One thing that assists in automatic environmental monitoring is leaf segmentation. By segmenting a leaf, image-based leaf health assessment can be performed which is crucial in maintaining the effectiveness of the environmental balance. This paper presents a technique that serves an accurate framework for diseased leaf segmentation from Coloured imaged. In other words, this method works to use information generated from RGB images that we have stored in our data base to represent the current input image. To achieve such technique, four main steps were constructed: 1) Using contrast variations to characterize the region of interest (ROI) of a given leaf which enhances the accuracy of the segmentation using minimal time. 2) using linear combination of discrete Gaussians (LCDG) to represent the visual appearance of the input image and to assume the marginal probability distributions of the three regions of interest classes. 3) Using information generated from RGB images that we have stored in our data base to calculate the probabilities of the three classes on a pixel basis in step two. 4) Lastly, clarifying the labels with Gauss-Markov random field model (GGMRF) to maintain the continuity. After all these steps, the experimental validation promised high accuracy.
{"title":"Precise Statistical Approach for Leaf Segmentation","authors":"M. Ghazal, Ali M. Mahmoud, A. Shalaby, Shams Shaker, A. Khelifi, A. El-Baz","doi":"10.1109/ICIP40778.2020.9190754","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9190754","url":null,"abstract":"One thing that assists in automatic environmental monitoring is leaf segmentation. By segmenting a leaf, image-based leaf health assessment can be performed which is crucial in maintaining the effectiveness of the environmental balance. This paper presents a technique that serves an accurate framework for diseased leaf segmentation from Coloured imaged. In other words, this method works to use information generated from RGB images that we have stored in our data base to represent the current input image. To achieve such technique, four main steps were constructed: 1) Using contrast variations to characterize the region of interest (ROI) of a given leaf which enhances the accuracy of the segmentation using minimal time. 2) using linear combination of discrete Gaussians (LCDG) to represent the visual appearance of the input image and to assume the marginal probability distributions of the three regions of interest classes. 3) Using information generated from RGB images that we have stored in our data base to calculate the probabilities of the three classes on a pixel basis in step two. 4) Lastly, clarifying the labels with Gauss-Markov random field model (GGMRF) to maintain the continuity. After all these steps, the experimental validation promised high accuracy.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128974280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9191093
Simying Ong, Koksheik Wong
This paper proposes an information hiding method to embed data while executing image enhancement steps. The 2D Median Filter is adapted and re-engineered to demonstrate the feasibility of this concept. In particular, the filtering-embedding steps are performed for each pixel in a sliding window manner. Pixels enclosed within the predefined window (neighborhood) are gathered, linearized and sorted. Then, the linearized pixels are divided into partitions, in which each partition is assigned to represent a certain sequence of bits. The performance of the proposed method is evaluated by using the BSD300 dataset for various settings. The embedding capacity, image quality, data extraction error rate are reported and analyzed. Besides, the robustness of the proposed method against brute force attack is also discussed. In the best case scenario, when the window size is $7 times 7, sim 0.97$ bpp is achieved with acceptable image quality while having $sim 3.5$% data extraction error rate.
{"title":"Information Hiding In Image Enhancement","authors":"Simying Ong, Koksheik Wong","doi":"10.1109/ICIP40778.2020.9191093","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9191093","url":null,"abstract":"This paper proposes an information hiding method to embed data while executing image enhancement steps. The 2D Median Filter is adapted and re-engineered to demonstrate the feasibility of this concept. In particular, the filtering-embedding steps are performed for each pixel in a sliding window manner. Pixels enclosed within the predefined window (neighborhood) are gathered, linearized and sorted. Then, the linearized pixels are divided into partitions, in which each partition is assigned to represent a certain sequence of bits. The performance of the proposed method is evaluated by using the BSD300 dataset for various settings. The embedding capacity, image quality, data extraction error rate are reported and analyzed. Besides, the robustness of the proposed method against brute force attack is also discussed. In the best case scenario, when the window size is $7 times 7, sim 0.97$ bpp is achieved with acceptable image quality while having $sim 3.5$% data extraction error rate.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129054448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9190965
Siyao Zhou, Xin Jin, Pei Wang
VR image in form of the spherical panoramic image is already widely available while enhancing its immersive experience with six degrees of freedom (6-DoF) is fundamentally required. Spherical panoramic light field (LF) becomes a potential solution because of recording the spatial and angular information of the light rays in the 360° spherical space. In this paper, a novel method is proposed to generate spherical panoramic LF by stitching LFs captured at different rotational angles. First, concentric spherical modeling is proposed to parameterize the recorded rays to eliminate the projection biases in registration. Then, the concentric spherical model-based LF registration which is insensitive to the ordering is introduced to transform each 4D LFs mesh accurately. Finally, the stitching result is projected to Two-parallel-plane (TPP) coordinates for viewing. Experimental results show that the proposed method outperforms the existing methods in terms of subjective quality and continuity in the stitched LF.
{"title":"Light Field Stitching Based On Concentric Spherical Modeling","authors":"Siyao Zhou, Xin Jin, Pei Wang","doi":"10.1109/ICIP40778.2020.9190965","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9190965","url":null,"abstract":"VR image in form of the spherical panoramic image is already widely available while enhancing its immersive experience with six degrees of freedom (6-DoF) is fundamentally required. Spherical panoramic light field (LF) becomes a potential solution because of recording the spatial and angular information of the light rays in the 360° spherical space. In this paper, a novel method is proposed to generate spherical panoramic LF by stitching LFs captured at different rotational angles. First, concentric spherical modeling is proposed to parameterize the recorded rays to eliminate the projection biases in registration. Then, the concentric spherical model-based LF registration which is insensitive to the ordering is introduced to transform each 4D LFs mesh accurately. Finally, the stitching result is projected to Two-parallel-plane (TPP) coordinates for viewing. Experimental results show that the proposed method outperforms the existing methods in terms of subjective quality and continuity in the stitched LF.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130348977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9191204
V. Katkovnik, I. Shevkunov, K. Egiazarian
Hyperspectral (HS) imaging retrieves information from data obtained across a wide spectral range of spectral channels. The object to reconstruct is a 3D cube, where two coordinates are spatial and third one is spectral. We assume that this cube is complex-valued, i.e. characterized spatially frequency varying amplitude and phase. The observations are squared magnitudes measured as intensities summarized over spectrum. The HS phase retrieval problem is formulated as a reconstruction of the HS complex-valued object cube from Gaussian noisy intensity observations. The derived iterative algorithm includes the original proximal spectral analysis operator and the sparsity modeling for complex-valued 3D cubes. The efficiency of the algorithm is confirmed by simulation tests.
{"title":"Broadband Hyperspectral Phase Retrieval From Noisy Data","authors":"V. Katkovnik, I. Shevkunov, K. Egiazarian","doi":"10.1109/ICIP40778.2020.9191204","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9191204","url":null,"abstract":"Hyperspectral (HS) imaging retrieves information from data obtained across a wide spectral range of spectral channels. The object to reconstruct is a 3D cube, where two coordinates are spatial and third one is spectral. We assume that this cube is complex-valued, i.e. characterized spatially frequency varying amplitude and phase. The observations are squared magnitudes measured as intensities summarized over spectrum. The HS phase retrieval problem is formulated as a reconstruction of the HS complex-valued object cube from Gaussian noisy intensity observations. The derived iterative algorithm includes the original proximal spectral analysis operator and the sparsity modeling for complex-valued 3D cubes. The efficiency of the algorithm is confirmed by simulation tests.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130637499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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