  Image textures, as a kind of local variations, provide important information
for human visual system. Many image textures, especially the small-scale or
stochastic textures are rich in high-frequency variations, and are difficult to
be preserved. Current state-of-the-art denoising algorithms typically adopt a
nonlocal approach consisting of image patch grouping and group-wise denoising
filtering. To achieve a better image denoising while preserving the variations
in texture, we first adaptively group high correlated image patches with the
same kinds of texture elements (texels) via an adaptive clustering method. This
adaptive clustering method is applied in an
over-clustering-and-iterative-merging approach, where its noise robustness is
improved with a custom merging threshold relating to the noise level and
cluster size. For texture-preserving denoising of each cluster, considering
that the variations in texture are captured and wrapped in not only the
between-dimension energy variations but also the within-dimension variations of
PCA transform coefficients, we further propose a PCA-transform-domain variation
adaptive filtering method to preserve the local variations in textures.
Experiments on natural images show the superiority of the proposed
transform-domain variation adaptive filtering to traditional PCA-based hard or
soft threshold filtering. As a whole, the proposed denoising method achieves a
favorable texture preserving performance both quantitatively and visually,
especially for stochastic textures, which is further verified in camera raw
image denoising.
