The paper examines an application of the gradient-based algorithm to image denoising with noise values being in the range of the available (non-noisy) pixel values. The analyzed image is considered to be sparse in the 2D-DCT domain. The presented algorithm is a generalization of the previous results on denoising images when the noisy pixels can be detected and eliminated using the L-statistics. The algorithm is based on the recently developed technique used for denoising of one-dimensional signals. A significant advantage of the presented algorithm is that it does not use any a priori knowledge about the positions, values or distribution of the noisy pixels. It is assumed that the positions of noisy pixels cannot be determined using the methods like the L-statistics based ones. Hence, the proposed approach reconstructs the pixels values iteratively using the highest gradient as pixel selection criterion, thus performing blind denoising on a pixel-by-pixel basis. The examples with synthetic two-dimensional signal and a test image are presented. Quality of the image reconstruction is measured using the structural similarity index and the mean absolute error (MAE).
['Isidora Stankovic', 'Irena Orovic', 'Srdjan Stankovic', 'Milos Dakovic']
Iterative denoising of sparse images