  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. Current existing denoising
methods have their own assumptions on the probability distribution in which the
contaminated noise is sampled for the method to attain its expected denoising
performance. In this paper, we utilize our recent Gramian-based filtering
scheme to remove noise sampled from five prominent probability distributions
from selected images. 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. We validate its denoising performance, using
three benchmark computer vision test images applied to two state-of-the-art
denoising methods, namely BM3D and K-SVD.
