A unified block-based sparse domain solution for quasi-periodic de-noising from different genres of images with iterative filtering

Abstract

Images, corresponding to various crucial imagery applications often experience stern problem of being degraded by different modalities of periodic/quasi-periodic noises. Though few periodic denoising algorithms address well for some specific application only, most of them fail to focus on the problem as a whole. In this article, a unified solution is presented which performs well for most of the vital non-natural imagery applications having dissimilar modalities. Initially, we divide the corrupted image into several blocks and then average those to get an averaged spatial image block. This block gets convolved with the Kaiser-Window to avoid any unnecessary artifacts followed by the spectral domain transformation. Our proposed algorithm relies on steadily decreasing characteristic of any uncorrupted natural image’s power spectra to expect a model by grossly reducing induced noise. An image feature based adaptive threshold is then applied on error spectra to precisely perceive unexpectedly high spectral amplitudes as the outliers. It is then interpolated to the actual size of the corrupted image, containing noisy spectra on which a proposed recursively adaptive notch-reject filter is applied. Extensive and detailed study of performance comparison with other state-of-the-art algorithms proves the supremacy of our proposed strategy.