Color Image Denoising Methods: A Laconic Review

Color image denoising is an essential process in image processing, intending to remove noise from images while preserving the important image details, for instance, edges, resolution, and accuracy. This paper presents an experiment-based review of the recent methods of color image denoising algorithms, focusing on their strengths, limitations, complexity, findings, accuracy, and comparative performance. Therefore, eight methods in color image denoising with different concepts were reviewed and evaluated under a reliable experimental environment. The evaluation was conducted using a dataset collected from three different sources, such as a professional DSLR camera, various mobile devices, and the MIT-Adobe database, tested under different real-world noise conditions. The reviewed methods are assessed by three preceding metrics were selected as no-reference metrics to evaluate real color images where clean reference images are unavailable: fast image sharpness estimation (FISH), no-reference structure similarity (NRSS), sparsity, and dominant-orientation quality index (SDQI), objectively, along with subjective visual analysis. The results demonstrate that the Total Variation with Split Bregman (TVSB) algorithm achieved the highest performance and exceeded the other methods. Reviewed methods showed competitive results in fine structure, details, and preserving edges. Additionally, the study discusses future recommendations for improving the effectiveness of these algorithms. Finally, this research is carried out systematically and empirically and focuses on the merits and demerits of their performance. It provides stepwise guidance on how to systematically target a particular approach in the color image denoising process, which highlights the practical and theoretical disparity. Moreover, it serves as a rich and source for scholars intending to develop algorithms in this domain.