# Total Variation with Overlapping Group Sparsity and Lp Quasinorm for Infrared Image Deblurring under Salt-and-Pepper Noise

## TL;DR

This paper introduces a novel infrared image deblurring method combining overlapping group sparsity, Lp quasinorm, and accelerated ADMM, effectively reducing noise and preserving details.

## Contribution

It proposes using Lp quasinorm instead of L1 norm with overlapping group sparsity for improved infrared image deblurring, enhancing sparsity modeling and restoration quality.

## Key findings

- Outperforms existing methods in objective metrics
- Effectively reduces salt-and-pepper noise
- Preserves image details and edges

## Abstract

Because of the limitations of the infrared imaging principle and the properties of infrared imaging systems, infrared images have some drawbacks, including a lack of details, indistinct edges, and a large amount of salt-andpepper noise. To improve the sparse characteristics of the image while maintaining the image edges and weakening staircase artifacts, this paper proposes a method that uses the Lp quasinorm instead of the L1 norm and for infrared image deblurring with an overlapping group sparse total variation method. The Lp quasinorm introduces another degree of freedom, better describes image sparsity characteristics, and improves image restoration. Furthermore, we adopt the accelerated alternating direction method of multipliers and fast Fourier transform theory in the proposed method to improve the efficiency and robustness of our algorithm. Experiments show that under different conditions for blur and salt-and-pepper noise, the proposed method leads to excellent performance in terms of objective evaluation and subjective visual results.