Title: Good Similar Patches for Image Denoising,
Abstract: Patch-based denoising algorithms like BM3D have achieved outstanding
performance. An important idea for the success of these methods is to exploit
the recurrence of similar patches in an input image to estimate the underlying
image structures. However, in these algorithms, the similar patches used for
denoising are obtained via Nearest Neighbour Search (NNS) and are sometimes not
optimal. First, due to the existence of noise, NNS can select similar patches
with similar noise patterns to the reference patch. Second, the unreliable
noisy pixels in digital images can bring a bias to the patch searching process
and result in a loss of color fidelity in the final denoising result. We
observe that given a set of good similar patches, their distribution is not
necessarily centered at the noisy reference patch and can be approximated by a
Gaussian component. Based on this observation, we present a patch searching
method that clusters similar patch candidates into patch groups using Gaussian
Mixture Model-based clustering, and selects the patch group that contains the
reference patch as the final patches for denoising. We also use an unreliable
pixel estimation algorithm to pre-process the input noisy images to further
improve the patch searching. Our experiments show that our approach can better
capture the underlying patch structures and can consistently enable the
state-of-the-art patch-based denoising algorithms, such as BM3D, LPCA and PLOW,
to better denoise images by providing them with patches found by our approach
while without modifying these algorithms.