An [formula omitted]-norm based color image deblurring model under mixed random-valued impulse and Gaussian noise

Abstract

This article introduces a novel model that restores color images degraded by blurring and mixed random-valued (RV) impulse and Gaussian noise. The model includes a data-fidelity term that consists of RV impulse noise and Gaussian noise components, and total variation regularization. Specifically, the data-fidelity term uses the l0- and l2-norms to represent the RV impulse and Gaussian noise components respectively. This results in an appropriate separation of these two noise components and thus enables simultaneous elimination of the mixed noise. Moreover, total variation regularization allows sufficient denoising in homogeneous regions while maintaining edges. To solve the nonconvex and nonsmooth model, we adopt a proximal alternating minimization approach and the alternating direction method of multipliers. These techniques contribute to an iterative algorithm with a convergence analysis provided. The experimental results demonstrate the effectiveness of the proposed model compared to other existing models, as measured by visual inspection and image quality assessments.