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Because of the limitations of the infrared imaging principle and theproperties of infrared imaging systems, infrared images have some drawbacks,including a lack of details, indistinct edges, and a large amount ofsalt-andpepper noise. To improve the sparse characteristics of the image whilemaintaining the image edges and weakening staircase artifacts, this paperproposes a method that uses the Lp quasinorm instead of the L1 norm and forinfrared image deblurring with an overlapping group sparse total variationmethod. The Lp quasinorm introduces another degree of freedom, better describesimage sparsity characteristics, and improves image restoration. Furthermore, weadopt the accelerated alternating direction method of multipliers and fastFourier transform theory in the proposed method to improve the efficiency androbustness of our algorithm. Experiments show that under different conditionsfor blur and salt-and-pepper noise, the proposed method leads to excellentperformance in terms of objective evaluation and subjective visual results.

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