MDPI - Publisher of Open Access Journals

15 pages, 2197 KiB

Open AccessArticle

Generalized Non-Convex Non-Smooth Group-Sparse Residual Prior for Image Denoising

by Shaohe Wang, Rui Han, Ping Qian and Chen Li

Electronics 2025, 14(2), 353; https://doi.org/10.3390/electronics14020353 - 17 Jan 2025

Viewed by 840

Image denoising is a classic yet challenging problem in low-level image processing. Traditional image denoising approaches using convex regularized prior (e.g.,

L_{1}

-norm) often bring bias problems. To address this issue, a novel prior model based on a family of non-convex functions [...] Read more.

Image denoising is a classic yet challenging problem in low-level image processing. Traditional image denoising approaches using convex regularized prior (e.g.,

L_{1}

-norm) often bring bias problems. To address this issue, a novel prior model based on a family of non-convex functions and group sparsity residual (GSC) prior constraint for image denoising is studied. We propose a generalized non-convex GSC prior model for the image denoising problem. We first utilize the group-sparse representation (GSR) before exploiting image prior information. Specifically, to further improve the image denoising performance of the GSC prior model, we employ several typical non-convex surrogate functions for the sparsity constraint. Then, a fast and efficient thresholding algorithm is proposed to minimize the resulting optimization problem. The experimental results have demonstrated that our proposed method can achieve the best reconstruction results compared with other image denoising approaches. Full article

(This article belongs to the Section Electronic Multimedia)

► Show Figures

Figure 1

15 pages, 2929 KiB

Open AccessArticle

Multi-Color Channels Based Group Sparse Model for Image Restoration

by Yanfen Kong, Caiyue Zhou, Chuanyong Zhang, Lin Sun and Chongbo Zhou

Algorithms 2022, 15(6), 176; https://doi.org/10.3390/a15060176 - 24 May 2022

Cited by 1 | Viewed by 1914

Abstract

The group sparse representation (GSR) model combines local sparsity and nonlocal similarity in image processing, and achieves excellent results. However, the traditional GSR model and all subsequent improved GSR models convert the RGB space of the image to YCbCr space, and only extract the Y (luminance) channel of YCbCr space to change the color image to a gray image for processing. As a result, the image processing process cannot be loyal to each color channel, so the repair effect is not ideal. A new group sparse representation model based on multi-color channels is proposed in this paper. The model processes R, G and B color channels simultaneously when processing color images rather than processing a single color channel and then combining the results of different channels. The proposed multi-color-channels-based GSR model is compared with state-of-the-art methods. The experimental contrast results show that the proposed model is an effective method and can obtain good results in terms of objective quantitative metrics and subjective visual effects. Full article

► Show Figures

Figure 1

21 pages, 24935 KiB

Open AccessArticle

A Hybrid Sparse Representation Model for Image Restoration

by Caiyue Zhou, Yanfen Kong, Chuanyong Zhang, Lin Sun, Dongmei Wu and Chongbo Zhou

Sensors 2022, 22(2), 537; https://doi.org/10.3390/s22020537 - 11 Jan 2022

Cited by 2 | Viewed by 2447

Abstract

Group-based sparse representation (GSR) uses image nonlocal self-similarity (NSS) prior to grouping similar image patches, and then performs sparse representation. However, the traditional GSR model restores the image by training degraded images, which leads to the inevitable over-fitting of the data in the training model, resulting in poor image restoration results. In this paper, we propose a new hybrid sparse representation model (HSR) for image restoration. The proposed HSR model is improved in two aspects. On the one hand, the proposed HSR model exploits the NSS priors of both degraded images and external image datasets, making the model complementary in feature space and the plane. On the other hand, we introduce a joint sparse representation model to make better use of local sparsity and NSS characteristics of the images. This joint model integrates the patch-based sparse representation (PSR) model and GSR model, while retaining the advantages of the GSR model and the PSR model, so that the sparse representation model is unified. Extensive experimental results show that the proposed hybrid model outperforms several existing image recovery algorithms in both objective and subjective evaluations. Full article

(This article belongs to the Section Sensing and Imaging)

► Show Figures

Figure 1

18 pages, 44978 KiB

Open AccessArticle

Group-Based Sparse Representation for Compressed Sensing Image Reconstruction with Joint Regularization

by Rongfang Wang, Yali Qin, Zhenbiao Wang and Huan Zheng

Electronics 2022, 11(2), 182; https://doi.org/10.3390/electronics11020182 - 7 Jan 2022

Cited by 9 | Viewed by 2722

Abstract

Achieving high-quality reconstructions of images is the focus of research in image compressed sensing. Group sparse representation improves the quality of reconstructed images by exploiting the non-local similarity of images; however, block-matching and dictionary learning in the image group construction process leads to a long reconstruction time and artifacts in the reconstructed images. To solve the above problems, a joint regularized image reconstruction model based on group sparse representation (GSR-JR) is proposed. A group sparse coefficients regularization term ensures the sparsity of the group coefficients and reduces the complexity of the model. The group sparse residual regularization term introduces the prior information of the image to improve the quality of the reconstructed image. The alternating direction multiplier method and iterative thresholding algorithm are applied to solve the optimization problem. Simulation experiments confirm that the optimized GSR-JR model is superior to other advanced image reconstruction models in reconstructed image quality and visual effects. When the sensing rate is 0.1, compared to the group sparse residual constraint with a nonlocal prior (GSRC-NLR) model, the gain of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) is up to 4.86 dB and 0.1189, respectively. Full article

(This article belongs to the Collection Graph Machine Learning)

► Show Figures

Figure 1

19 pages, 5983 KiB

Open AccessArticle

Group Sparse Representation Based on Nonlocal Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification

by Haoyang Yu, Lianru Gao, Wenzhi Liao and Bing Zhang

Sensors 2018, 18(6), 1695; https://doi.org/10.3390/s18061695 - 24 May 2018

Cited by 16 | Viewed by 4472

Abstract

Spectral-spatial classification has been widely applied for remote sensing applications, especially for hyperspectral imagery. Traditional methods mainly focus on local spatial similarity and neglect nonlocal spatial similarity. Recently, nonlocal self-similarity (NLSS) has gradually gained support since it can be used to support spatial coherence tasks. However, these methods are biased towards the direct use of spatial information as a whole, while discriminative spectral information is not well exploited. In this paper, we propose a novel method to couple both nonlocal spatial and local spectral similarity together in a single framework. In particular, the proposed approach exploits nonlocal spatial similarities by searching non-overlapped patches, whereas spectral similarity is analyzed locally within the locally discovered patches. By fusion of nonlocal and local information, we then apply group sparse representation (GSR) for classification based on a group structured prior. Experimental results on three real hyperspectral data sets demonstrate the efficiency of the proposed approach, and the improvements are significant over the methods that consider either nonlocal or local similarity. Full article

(This article belongs to the Special Issue Spatial Analysis and Remote Sensing)

► Show Figures

Figure 1

Search Results (5)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI

Saved Queries

Search Filter Reset All

Years

Feature Papers

Subjects

Journals

Article Types

Countries / Regions

Search Results (5)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI