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