Sparse Technique for Images Corrupted by Mixed Gaussian-Impulsive Noise

In this paper, a novel framework is presented for denoising images that have been corrupted by a mixture of additive and impulsive noise. The proposed method consists of three main stages: impulsive noise suppression, additive noise suppression and post-processing. In the first stage, a pixel that has been contaminated by impulsive noise is detected and filtered. In the next stage, filtering is based on sparse representation and 3D-processing using discrete cosine transform. Finally, the post-processing stage increases the filtering quality by using a bilateral filter and an edge restoration technique. Evaluation is performed using objective criteria (PSNR and SSIM) and subjective human visual perception to confirm the methods performance compared with state-of-the-art techniques.