First-order primal–dual algorithm for image restoration corrupted by mixed Poisson–Gaussian noise

• DOI: 10.1016/j.image.2023.117012
• Publication Date: 2023-07-11T15:41:33Z
• Authors: Miao Chen; Meng Wen; Yuchao Tang
• Keywords: Deblurring; Total variation denoising; Gaussian Noise; Convolution (computer science); Divergence (linguistics)

The total variation infimal convolution (TV-IC) model combining Kullback–Leibler and ℓ2-norm data fidelity term works well for the inverse problem of mixed Poisson–Gaussian noise. Most existing algorithms solving TV-IC rely on Newton method to solve a nonlinear optimization subproblem, which inevitably increases computation burden. In this study, we apply first-order primal–dual Chambolle-Pock algorithm model. particular, present an effective subproblem joint proximal operator with divergence ℓ2-norm, is based bilinear constraint alternating direction multiplier method. Numerical experiment results demonstrate that proposed outperforms state-of-the-art methods denoising deblurring problems.