Deep Sparse Representation Based Image Restoration With Denoising Prior

Xu, Wei; Zhu, Qingsheng; Qi, Na; Chen, Dongpan

doi:10.1109/tcsvt.2022.3170689

Cited by 24 publications

(3 citation statements)

References 64 publications

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“…Huang et al [75] proposed a nonlocal self-similar (NSS) block-based deep image denoising scheme, designated the deep low-rank prior (DLRP), to achieve efficient performance. Xu et al [76] developed an end-to-end deep architecture to follow the process of sparse-representation-based image restoration.…”

Section: Deep Unfoldingmentioning

confidence: 99%

MD3: Model-Driven Deep Remotely Sensed Image Denoising

et al. 2023

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Remotely sensed images degraded by additive white Gaussian noise (AWGN) have low-level vision, resulting in a poor analysis of their contents. To reduce AWGN, two types of denoising strategies, sparse-coding-model-based and deep-neural-network-based (DNN), are commonly utilized, which have their respective merits and drawbacks. For example, the former pursue enjoyable performance with a high computational burden, while the latter have powerful capacity in completing a specified task efficiently, but this limits their application range. To combine their merits for improving performance efficiently, this paper proposes a model-driven deep denoising (MD3) scheme. To solve the MD3 model, we first decomposed it into several subproblems by the alternating direction method of multipliers (ADMM). Then, the denoising subproblems are replaced by different learnable denoisers, which are plugged into the unfolded MD3 model to efficiently produce a stable solution. Both quantitative and qualitative results validate that the proposed MD3 approach is effective and efficient, while it has a more powerful ability in generating enjoyable denoising performance and preserving rich textures than other advanced methods.

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Section: Deep Unfoldingmentioning

confidence: 99%

MD3: Model-Driven Deep Remotely Sensed Image Denoising

et al. 2023

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“…However, in recent years, the performance of CSC has been surpassed by deep learning. Building a "deep" CSC model has potential application values in various fields such as image restoration [15,26], image classification [27], and image registration [28].…”

Section: Convolutional Sparse Modellingmentioning

confidence: 99%

Multi-Layer Deep Sparse Representation for Biological Slice Image Inpainting

Hu,

Ma,

Mei

2023

Computers, Materials &Amp; Continua

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Biological slices are an effective tool for studying the physiological structure and evolution mechanism of biological systems. However, due to the complexity of preparation technology and the presence of many uncontrollable factors during the preparation processing, leads to problems such as difficulty in preparing slice images and breakage of slice images. Therefore, we proposed a biological slice image small-scale corruption inpainting algorithm with interpretability based on multi-layer deep sparse representation, achieving the high-fidelity reconstruction of slice images. We further discussed the relationship between deep convolutional neural networks and sparse representation, ensuring the high-fidelity characteristic of the algorithm first. A novel deep wavelet dictionary is proposed that can better obtain image prior and possess learnable feature. And multi-layer deep sparse representation is used to implement dictionary learning, acquiring better signal expression. Compared with methods such as NLABH, Shearlet, Partial Differential Equation (PDE), K-Singular Value Decomposition (K-SVD), Convolutional Sparse Coding, and Deep Image Prior, the proposed algorithm has better subjective reconstruction and objective evaluation with small-scale image data, which realized high-fidelity inpainting, under the condition of small-scale image data. And the O n 2 -level time complexity makes the proposed algorithm practical. The proposed algorithm can be effectively extended to other cross-sectional image inpainting problems, such as magnetic resonance images, and computed tomography images.

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“…Evidence demonstrates that image priors are the foundation for image restoration, including total variation (TV) [5][6][7], sparsity [2,8], low-rank [9][10][11], and deep image prior [12][13][14][15][16][17][18][19][20]. Particularly, sparsity prior is considered as one of the most remarkable for natural images [2,8,[21][22][23][24]. On the basis of the strategies for manipulating sparsity prior, current algorithms are roughly divided into two classes, that is, patch- [2,25,26] and group-based approaches [8,22,[27][28][29], where the former ones independently perform image restoration for each patch, and the latter ones execute restoration task for each group of patches.…”

Section: Introductionmentioning

confidence: 99%

Image restoration with group sparse representation and low‐rank group residual learning

Cai,

Xie,

Deng

et al. 2023

IET Image Processing

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Image restoration, as a fundamental research topic of image processing, is to reconstruct the original image from degraded signal using the prior knowledge of image. Group sparse representation (GSR) is powerful for image restoration; it however often leads to undesirable sparse solutions in practice. In order to improve the quality of image restoration based on GSR, the sparsity residual model expects the representation learned from degraded images to be as close as possible to the true representation. In this article, a group residual learning based on low‐rank self‐representation is proposed to automatically estimate the true group sparse representation. It makes full use of the relation among patches and explores the subgroup structures within the same group, which makes the sparse residual model have better interpretation furthermore, results in high‐quality restored images. Extensive experimental results on two typical image restoration tasks (image denoising and deblocking) demonstrate that the proposed algorithm outperforms many other popular or state‐of‐the‐art image restoration methods.

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Deep Sparse Representation Based Image Restoration With Denoising Prior

Cited by 24 publications

References 64 publications

MD3: Model-Driven Deep Remotely Sensed Image Denoising

MD3: Model-Driven Deep Remotely Sensed Image Denoising

Multi-Layer Deep Sparse Representation for Biological Slice Image Inpainting

Image restoration with group sparse representation and low‐rank group residual learning

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