Edge-preserving denoising using gradient-based estimation and iterative noise-aided processing

One of the major concerns while developing a good denoising algorithm is the preservation of edges. Thus, it is of prime importance to preserve the edges, corners and other sharp structures present in an image during the process of denoising. In this paper, the concept of stochastic resonance has been applied to denoise the noisy input image. In stochastic resonance, a small value of noise is added to the noisy input image iteratively to enhance the edges of the input image based on which the weight coefficient is determined. It is observed the quality of the output image improves as the number of iterations is increased up to a certain number of iteration after which the quality of the image degrades or remains constant depending on the value of noise in the input image. In order to compare the quality of the denoised output image wrt the number of iterations state of the art no-reference quality metric such as Blind Universal Quality Indices (BIQI), spatial-spectral entropy-based quality (SSEQ) have been used. When compared with conventional denoising techniques such as Anisotropic diffusion (AD), Bilateral filtering (BLT), adaptive smoothing, the proposed algorithm yields marginally better results.