In this supplementary material, we provide: 1. The closed-form solution of the proposed weighted sparse coding model in the main paper. 2. Comparison of the Patch Prior based Denoising (PPD) method and the proposed Patch Group Prior based Denoising (PGPD) method. 3. More denoising results on the 20 widely used natural images. 4. The denoising results of the competing methods on the Berkeley Segmentation Data Set [1]. 1. Closed-Form Solution of the Weighted Sparse Coding Problem The weighted sparse coding problem in the main paper is: min α ‖y −Dα‖22 + ‖wTα‖1. (1) Since D is an orthonormal matrix, problem (1) is equivalent to mi
We proposed a new efficient image denoising scheme, which leads to four important contributions. The...
Sparse prior provides an effective tool for the image reconstruction. However, the sparse coding for...
Patch-based denoising algorithms like BM3D have achieved outstanding performance. An important idea ...
Patch based image modeling has achieved a great suc-cess in low level vision such as image denoising...
Abstract Nonlocal image representation has been successfully used in many image-related inverse pro...
Good learning image priors from the noise-corrupted images or clean natural images are very importan...
Sparse coding is a challenging and promising theme in image denoising. Its main goal is to learn a s...
In the past decade, much progress has been made in image denoising due to the use of low-rank repres...
Abstract Sparse representation is a powerful statistical image modelling technique and has been succ...
Prior knowledge plays an important role in image denoising tasks. This paper utilizes the data of th...
Abstract — Here in this Paper a new algorithm probable nonlocal means (PNLM) method for image denois...
We proposed a new efficient image denoising scheme, which mainly leads to four important contributio...
We propose a differentiable algorithm for image restoration inspired by the success of sparse models...
The observed images are usually noisy due to data acquisition and transmission process. Therefore, i...
This thesis focuses on the topics of sparse and non-local signal and image processing. In particular...
We proposed a new efficient image denoising scheme, which leads to four important contributions. The...
Sparse prior provides an effective tool for the image reconstruction. However, the sparse coding for...
Patch-based denoising algorithms like BM3D have achieved outstanding performance. An important idea ...
Patch based image modeling has achieved a great suc-cess in low level vision such as image denoising...
Abstract Nonlocal image representation has been successfully used in many image-related inverse pro...
Good learning image priors from the noise-corrupted images or clean natural images are very importan...
Sparse coding is a challenging and promising theme in image denoising. Its main goal is to learn a s...
In the past decade, much progress has been made in image denoising due to the use of low-rank repres...
Abstract Sparse representation is a powerful statistical image modelling technique and has been succ...
Prior knowledge plays an important role in image denoising tasks. This paper utilizes the data of th...
Abstract — Here in this Paper a new algorithm probable nonlocal means (PNLM) method for image denois...
We proposed a new efficient image denoising scheme, which mainly leads to four important contributio...
We propose a differentiable algorithm for image restoration inspired by the success of sparse models...
The observed images are usually noisy due to data acquisition and transmission process. Therefore, i...
This thesis focuses on the topics of sparse and non-local signal and image processing. In particular...
We proposed a new efficient image denoising scheme, which leads to four important contributions. The...
Sparse prior provides an effective tool for the image reconstruction. However, the sparse coding for...
Patch-based denoising algorithms like BM3D have achieved outstanding performance. An important idea ...