Image Restoration Using Joint Patch-Group-Based Sparse Representation

2020 ◽  
Vol 29 ◽  
pp. 7735-7750 ◽  
Author(s):  
Zhiyuan Zha ◽  
Xin Yuan ◽  
Bihan Wen ◽  
Jiachao Zhang ◽  
Jiantao Zhou ◽  
...  
Keyword(s):  
Image Restoration ◽  
Sparse Representation ◽  
Patch Group

scholarly journals Simultaneous Patch-Group Sparse Coding with Dual-Weighted ℓp Minimization for Image Restoration

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1205
Author(s):  
Jiachao Zhang ◽  
Ying Tong ◽  
Liangbao Jiao
Keyword(s):  
Image Restoration ◽  
Sparse Representation ◽  
Sparse Coding ◽  
Image Inpainting ◽  
Perceptual Quality ◽  
Novel Approach ◽  
Smooth Effects ◽  
Convex Regularization ◽  
Generalized Iteration ◽  
Patch Group

Sparse coding (SC) models have been proven as powerful tools applied in image restoration tasks, such as patch sparse coding (PSC) and group sparse coding (GSC). However, these two kinds of SC models have their respective drawbacks. PSC tends to generate visually annoying blocking artifacts, while GSC models usually produce over-smooth effects. Moreover, conventional ℓ1 minimization-based convex regularization was usually employed as a standard scheme for estimating sparse signals, but it cannot achieve an accurate sparse solution under many realistic situations. In this paper, we propose a novel approach for image restoration via simultaneous patch-group sparse coding (SPG-SC) with dual-weighted ℓp minimization. Specifically, in contrast to existing SC-based methods, the proposed SPG-SC conducts the local sparsity and nonlocal sparse representation simultaneously. A dual-weighted ℓp minimization-based non-convex regularization is proposed to improve the sparse representation capability of the proposed SPG-SC. To make the optimization tractable, a non-convex generalized iteration shrinkage algorithm based on the alternating direction method of multipliers (ADMM) framework is developed to solve the proposed SPG-SC model. Extensive experimental results on two image restoration tasks, including image inpainting and image deblurring, demonstrate that the proposed SPG-SC outperforms many state-of-the-art algorithms in terms of both objective and perceptual quality.

scholarly journals A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP

Algorithms ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 174
Author(s):  
Sun ◽  
Zhang ◽  
Li ◽  
Meng
Keyword(s):  
Image Reconstruction ◽  
Radiation Dose ◽  
Image Restoration ◽  
Sparse Representation ◽  
Iterative Reconstruction ◽  
Model Parameters ◽  
Projection Data ◽  
Ct Image ◽  
High Quality ◽  
Ct Image Reconstruction

Computed tomography (CT) image reconstruction and restoration are very important in medical image processing, and are associated together to be an inverse problem. Image iterative reconstruction is a key tool to increase the applicability of CT imaging and reduce radiation dose. Nevertheless, traditional image iterative reconstruction methods are limited by the sampling theorem and also the blurring of projection data will propagate unhampered artifact in the reconstructed image. To overcome these problems, image restoration techniques should be developed to accurately correct a wide variety of image degrading effects in order to effectively improve image reconstruction. In this paper, a blind image restoration technique is embedded in the compressive sensing CT image reconstruction, which can result in a high-quality reconstruction image using fewer projection data. Because a small amount of data can be obtained by radiation in a shorter time, high-quality image reconstruction with less data is equivalent to reducing radiation dose. Technically, both the blurring process and the sparse representation of the sharp CT image are first modeled as a serial of parameters. The sharp CT image will be obtained from the estimated sparse representation. Then, the model parameters are estimated by a hierarchical Bayesian maximum posteriori formulation. Finally, the estimated model parameters are optimized to obtain the final image reconstruction. We demonstrate the effectiveness of the proposed method with the simulation experiments in terms of the peak signal to noise ratio (PSNR), and structural similarity index (SSIM).

Sparse representation-based image restoration via nonlocal supervised coding

2016 ◽  
Vol 23 (5) ◽  
pp. 776-783 ◽  
Author(s):  
Ao Li ◽  
Deyun Chen ◽  
Guanglu Sun ◽  
Kezheng Lin
Keyword(s):  
Image Restoration ◽  
Sparse Representation

scholarly journals Sparse Representation based Satellite Image Restoration Using Adaptive Reciprocal Cell

2014 ◽  
Vol 9 (10) ◽  
pp. 341-348 ◽  
Author(s):  
Yanfei He ◽  
Jianwei Zhang ◽  
Shunfeng Wang ◽  
Yuhui Zheng ◽  
Jin Wang ◽  
...  
Keyword(s):  
Image Restoration ◽  
Sparse Representation ◽  
Satellite Image

scholarly journals Stable Sparse Model with Non-Tight Frame

2020 ◽  
Vol 10 (5) ◽  
pp. 1771 ◽  
Author(s):  
Min Zhang ◽  
Yunhui Shi ◽  
Na Qi ◽  
Baocai Yin
Keyword(s):  
Image Restoration ◽  
Sparse Representation ◽  
Tight Frame ◽  
Closed Form Expression ◽  
Dual Frame ◽  
Two Phase ◽  
Sparse Models ◽  
Frame Operator ◽  
Sparse Model ◽  
Sparse System

Overcomplete representation is attracting interest in image restoration due to its potential to generate sparse representations of signals. However, the problem of seeking sparse representation must be unstable in the presence of noise. Restricted Isometry Property (RIP), playing a crucial role in providing stable sparse representation, has been ignored in the existing sparse models as it is hard to integrate into the conventional sparse models as a regularizer. In this paper, we propose a stable sparse model with non-tight frame (SSM-NTF) via applying the corresponding frame condition to approximate RIP. Our SSM-NTF model takes into account the advantage of the traditional sparse model, and meanwhile contains RIP and closed-form expression of sparse coefficients which ensure stable recovery. Moreover, benefitting from the pair-wise of the non-tight frame (the original frame and its dual frame), our SSM-NTF model combines a synthesis sparse system and an analysis sparse system. By enforcing the frame bounds and applying a second-order truncated series to approximate the inverse frame operator, we formulate a dictionary pair (frame pair) learning model along with a two-phase iterative algorithm. Extensive experimental results on image restoration tasks such as denoising, super resolution and inpainting show that our proposed SSM-NTF achieves superior recovery performance in terms of both subjective and objective quality.

Image super-resolution via Gaussian scale patch group sparse representation

2018 ◽  
Vol 11 (2/3) ◽  
pp. 186
Author(s):  
Wu Minghu ◽  
Lu Yaqi ◽  
Zhao Nan ◽  
Liu Min ◽  
Liu Cong ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Super Resolution ◽  
Image Super Resolution ◽  
Patch Group

Blind moving image restoration based on sparse representation and Weber’s law

2015 ◽  
Vol 23 (2) ◽  
pp. 600-608 ◽  
Author(s):  
刘成云 LIU Cheng-yun ◽  
常发亮 CHANG Fa-liang
Keyword(s):  
Image Restoration ◽  
Sparse Representation ◽  
Weber’S Law ◽  
Moving Image ◽  
Weber's Law
Sign in / Sign up

Export Citation Format

Share Document