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

Jiachao Zhang1, Ying Tong1, Liangbao Jiao1,2

  • 1Artificial Intelligence Industrial Technology Research Institute, Nanjing Institute of Technology, Nanjing 211167, China.

Micromachines
|October 23, 2021
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Summary

This study introduces simultaneous patch-group sparse coding (SPG-SC) to enhance image restoration. The novel dual-weighted ℓp minimization approach overcomes limitations of existing sparse coding methods for clearer results.

Keywords:
ADMMSPG-SCdual-weighted ℓp minimizationimage restorationnonlocal sparse representationsparse coding

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Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Sparse coding (SC) models like patch sparse coding (PSC) and group sparse coding (GSC) are effective for image restoration.
  • PSC can cause blocking artifacts, while GSC may lead to over-smoothing.
  • Conventional ℓ1 minimization for sparse signal estimation has limitations in realistic scenarios.

Purpose of the Study:

  • To propose a novel simultaneous patch-group sparse coding (SPG-SC) approach for improved image restoration.
  • To address the drawbacks of existing SC models by combining local and nonlocal sparse representations.
  • To enhance sparse representation capability using dual-weighted ℓp minimization.

Main Methods:

  • Developed a simultaneous patch-group sparse coding (SPG-SC) model.
  • Introduced a dual-weighted ℓp minimization for non-convex regularization.
  • Utilized an alternating direction method of multipliers (ADMM) framework for a generalized iteration shrinkage algorithm.

Main Results:

  • The proposed SPG-SC model simultaneously exploits local sparsity and nonlocal sparse representation.
  • Dual-weighted ℓp minimization improved the sparse representation accuracy.
  • Experimental results on image inpainting and deblurring showed superior performance compared to state-of-the-art methods.

Conclusions:

  • The novel SPG-SC approach with dual-weighted ℓp minimization effectively enhances image restoration.
  • The method achieves better objective and perceptual quality than existing algorithms.
  • The developed ADMM-based optimization is tractable and efficient for the proposed model.