- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 International Joint Conference on Neural Networks (IJCNN) - Rio de Janeiro (2018.7.8-2018.7.13)] 2018 International Joint Conference on Neural Networks (IJCNN) - Multi-spectral missing label prediction via restoration using deep residual dictionary learning
摘要： Dictionary learning (DL) is one of the popular sparse coding machine learning techniques. In image processing literature, every input image is represented as the sparse linear combination of basis vectors. DL has been shown to have wide applications for image restoration as well as pattern recognition problems. In DL, the input image is factorized into dictionary and sparse codes. This factorization always leaves a residual or approximation error. Very few works in the literature had focused on to leverage the information present in this residual. In this paper, we use residuals within our framework and show that the restoration performance or accurate prediction of missing label in multi-spectral images can be significantly improved over conventional DL based techniques. We initially show that the higher order frequencies are propagated through residuals. Then we show that incorporating this residual in the image restoration methodology can significantly improve the outcomes. Finally, we propose a technique to solve the problem of missing label prediction by using a restoration based deep residual dictionary learning framework.
关键词： Dictionary learning,Restoration,Sparse coding,In-painting,Residuals,Label prediction
更新于2025-09-23 15:23:52
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[IEEE ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, AB (2018.4.15-2018.4.20)] 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Group Sparsity Residual with Non-Local Samples for Image Denoising
摘要： Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in GSR is to estimate the residual by using a proper reference of the group-based sparse coding (GSC), which is desired to be as close to the truth as possible. Previous researches utilized the estimations from other algorithms (i.e., GMM or BM3D), which are either not accurate or too slow. In this paper, we propose to use the Non-Local Samples (NLS) as reference in the GSR regime for image denoising, thus termed GSR-NLS. More specifically, we first obtain a good estimation of the group sparse coefficients by the image non-local self-similarity, and then solve the GSR model by an effective iterative shrinkage algorithm. Experimental results demonstrate that the proposed GSR-NLS not only outperforms many state-of-the-art methods, but also delivers the competitive advantage of speed.
关键词： Image denoising,group sparsity residual,iterative shrinkage algorithm,group-based sparse coding,non-local self-similarity
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) - Aristi Village, Zagorochoria, Greece (2018.6.10-2018.6.12)] 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) - A Fast Parallel Algorithm for Convolutional Sparse Coding
摘要： The current leading algorithms for convolutional sparse coding are not inherently parallelizable, and therefore are not able to fully exploit modern multi-core architectures. We address this deficiency by developing a new algorithm that partitions the dictionary and the corresponding coefficient maps into groups, solving the main subproblems for all of the groups in parallel. Theoretical complexities and implementational details are discussed and validated with computational experiments, which indicate speed improvements by about a factor of 5, depending on the specific problem.
关键词： Convolutional Sparse Representations,ADMM,Convolutional Sparse Coding
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Sparse-Coding Adapted to SAR Images with an Application to Despeckling
摘要： In this paper, we propose a sparsity-based despeckling approach. The first main contribution of this work is the elaboration of a sparse-coding algorithm adapted to the statistics of SAR images. In fact, most sparse-coding algorithms for SAR data apply a logarithmic transform to data, so as to convert the noise from multiplicative to additive. Then, a Gaussian prior is adopted. However, using a more suitable prior for SAR data avoids introducing artifacts. The second main contribution proposed is to predict the optimal sparsity degree for each patch based on local image features. Experiments show that this strategy improves upon traditional sparse coding with a low-error-rate stopping criterion.
关键词： SAR images,coefficient of variation,Patches,despeckling,sparse-coding
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - SAR Patch Categorization Using Stacked Sparse Coding
摘要： This paper presents Synthetic Aperture Radar (SAR) patch categorization using unsupervised feature learning framework. It is based on layer based sparse coding, which extends a sparse coding to a multilayer architecture. A contribution of this paper is a framework which consists of 3 layers of sparse coding, local spatial pooling layer, normalization layer, map reduction layer and a classification layer. The new method is able to learn several levels of sparse representation of the image which capture features at a variety of abstraction levels and simultaneously preserve the spatial smoothness between the neighboring image patches. The proposed method achieved promising results in SAR patch categorization.
关键词： classification,Synthetic Aperture Radar,sparse coding,Categorization
更新于2025-09-23 15:22:29
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Hyperspectral Image Denoising Based on Spectral Dictionary Learning and Sparse Coding
摘要： Processing and applications of hyperspectral images (HSI) are limited by the noise component. This paper establishes an HSI denoising algorithm by applying dictionary learning and sparse coding theory, which is extended into the spectral domain. First, the HSI noise model under additive noise assumption was studied. Considering the spectral information of HSI data, a novel dictionary learning method based on an online method is proposed to train the spectral dictionary for denoising. With the spatial–contextual information in the noisy HSI exploited as a priori knowledge, the total variation regularizer is introduced to perform the sparse coding. Finally, sparse reconstruction is implemented to produce the denoised HSI. The performance of the proposed approach is better than the existing algorithms. The experiments illustrate that the denoising result obtained by the proposed algorithm is at least 1 dB better than that of the comparison algorithms. The intrinsic details of both spatial and spectral structures can be preserved after significant denoising.
关键词： image processing,hyperspectral image,spectral dictionary,image denoising,sparse coding
更新于2025-09-23 15:22:29
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Nonlocal Patch Tensor Sparse Representation for Hyperspectral Image Super-Resolution
摘要： This paper presents a hyperspectral image (HSI) super-resolution method which fuses a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor-tensor product (t-product) based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and LR-HSI is built using t-product which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, Alternating Direction Method of Multipliers (ADMM) is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-of-the-art HSI super-resolution methods.
关键词： tensor dictionary learning,Hyperspectral image,nonlocal patch tensor,tensor sparse coding,super-resolution
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) - Aalborg, Denmark (2018.9.17-2018.9.20)] 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) - K-SVD with a Real ?<inf>0</inf> Optimization: Application to Image Denoising
摘要： This paper deals with sparse coding for dictionary learning in sparse representations. Because sparse coding involves an (cid:96)0-norm, most, if not all, existing solutions only provide an approximate solution. Instead, in this paper, a real (cid:96)0 optimization is considered for the sparse coding problem providing a global optimal solution. The proposed method reformulates the optimization problem as a Mixed-Integer Quadratic Program (MIQP), allowing then to obtain the global optimal solution by using an off-the-shelf solver. Because computing time is the main disadvantage of this approach, two techniques are proposed to improve its computational speed. One is to add suitable constraints and the other to use an appropriate initialization. The results obtained on an image denoising task demonstrate the feasibility of the MIQP approach for processing well-known benchmark images while achieving good performance compared with the most advanced methods.
关键词： image denoising,dictionary learning,Mixed-integer quadratic programming,sparse representation,K-SVD,sparse coding
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Ge virtual substrates for high efficiency III-V solar cells: applications, potential and challenges
摘要： Motion capture is an important technique with a wide range of applications in areas such as computer vision, computer animation, ?lm production, and medical rehabilita- tion. Even with the professional motion capture systems, the acquired raw data mostly contain inevitable noises and outliers. To denoise the data, numerous methods have been developed, while this problem still remains a challenge due to the high com- plexity of human motion and the diversity of real-life situations. In this paper, we propose a data-driven-based robust human motion denoising approach by mining the spatial-temporal pat- terns and the structural sparsity embedded in motion data. We ?rst replace the regularly used entire pose model with a much ?ne-grained partlet model as feature representation to exploit the abundant local body part posture and movement similari- ties. Then, a robust dictionary learning algorithm is proposed to learn multiple compact and representative motion dictionaries from the training data in parallel. Finally, we reformulate the human motion denoising problem as a robust structured sparse coding problem in which both the noise distribution informa- tion and the temporal smoothness property of human motion have been jointly taken into account. Compared with several state-of-the-art motion denoising methods on both the synthetic and real noisy motion data, our method consistently yields better performance than its counterparts. The outputs of our approach are much more stable than that of the others. In addition, it is much easier to setup the training dataset of our method than that of the other data-driven-based methods.
关键词： (cid:2)2,p-norm,robust dictionary learning,Microsoft Kinect,robust structured sparse coding,motion capture data,Human motion denoising
更新于2025-09-23 15:19:57
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[IEEE 2019 Photonics North (PN) - Quebec City, QC, Canada (2019.5.21-2019.5.23)] 2019 Photonics North (PN) - Human Cardiac Tissue Collagen Polarity Revealed Using Polarimetric Second-Harmonic Generation Microscopy
摘要： Motion capture is an important technique with a wide range of applications in areas such as computer vision, computer animation, ?lm production, and medical rehabilita- tion. Even with the professional motion capture systems, the acquired raw data mostly contain inevitable noises and outliers. To denoise the data, numerous methods have been developed, while this problem still remains a challenge due to the high com- plexity of human motion and the diversity of real-life situations. In this paper, we propose a data-driven-based robust human motion denoising approach by mining the spatial-temporal pat- terns and the structural sparsity embedded in motion data. We ?rst replace the regularly used entire pose model with a much ?ne-grained partlet model as feature representation to exploit the abundant local body part posture and movement similari- ties. Then, a robust dictionary learning algorithm is proposed to learn multiple compact and representative motion dictionaries from the training data in parallel. Finally, we reformulate the human motion denoising problem as a robust structured sparse coding problem in which both the noise distribution informa- tion and the temporal smoothness property of human motion have been jointly taken into account. Compared with several state-of-the-art motion denoising methods on both the synthetic and real noisy motion data, our method consistently yields better performance than its counterparts. The outputs of our approach are much more stable than that of the others. In addition, it is much easier to setup the training dataset of our method than that of the other data-driven-based methods.
关键词： Microsoft Kinect,robust structured sparse coding,Human motion denoising,motion capture data,robust dictionary learning,(cid:2)2,p-norm
更新于2025-09-19 17:13:59