A proficient method for periodic and quasi-periodic noise fading using spectral histogram thresholding with sinc restoration filter

Abstract

Image denoising is a challenging task in the recent research field of image processing. During image acquisition, periodic and quasi-periodic noise perturbs the image signal. Hence, the area involving the removal of periodic noise from the images has immense significance among the researchers over the last few decades. Periodic noises are unintended signals and result in repetitive spatially dependent patterns. These degrade visual quality significantly. Presence of some high amplitude spiky peaks in the spectral domain makes them clearly distinguishable from the non-noisy coefficients. Hence, these become easy to segregate using appropriate thresholding technique. Till date, many algorithms have been proposed to alleviate noisy effect while preserving the authentic image information and thereby improving the image quality in frequency domain which has proved to be much better solution than the spatial domain operations. Here, we have proposed one simple, yet elegant, and fully adaptive reconstruction algorithm by using the concept of spectral domain histogram for thresholding. Then a novel sinc restoration filter is applied during the noisy frequencies cancellation phase. Performance of the proposed algorithm is compared with some other algorithms as discussed in the literature in terms of various metrics which proves the novelty and supremacy of our proposition both qualitatively and quantitatively.