Color image restoration with mixed Gaussian–Cauchy noise and blur

In this paper, we present a novel model for recovering color images that have been affected by mixed Gaussian Cauchy noise and blur. Our approach utilizes the l 1 -norm of the wavelet base as the regularization term and combines the Gaussian and Cauchy noise in a summation term as the data fidelity term. To solve this minimization model, we employ the alternating direction method of multipliers (ADMM). We evaluate the performance of our model by conducting experiments with different types of blur and mixed Gaussian–Cauchy noise. The experimental results demonstrate that our method surpasses other existing approaches in terms of PSNR values, SSIM values, and visual quality.