Geodesic Gramian denoising applied to the images contaminated with noise sampled from diverse probability distributions

As quotidian use of sophisticated cameras surges, people in modern society are more interested capturing fine-quality images. However, the quality images might be inferior to people's expectations due noise contamination Thus, filtering out while preserving vital image features is an essential requirement. Existing denoising methods have assumptions, on probability distribution which contaminated sampled, for method attain its expected performance. In this paper, recent Gramian-based scheme remove sampled from five prominent distributions selected utilized. This preserves smoothness by adopting patches partitioned image, rather than pixels, and retains performing manifold underlying patch space domain. Its performance validated, using six benchmark computer vision test applied two state-of-the-art methods, namely BM3D K-SVD.