Video denoising using shape-adaptive sparse representation over similar spatio-temporal patches

We present an effective patch-based video denoising algorithm that exploits both local and nonlocal correlations. The method groups 3D shape-adaptive patches, whose surrounding cubic neighborhoods along spatial and temporal dimensions have been found similar by patch clustering. Such grouping results in 4D data structures with arbitrary shapes. Since the obtained 4D groups are highly correlated along all the dimensions, they can be represented very sparsely with a 4D shape-adaptive DCT. The noise can be effectively attenuated by transform shrinkage. Experimental results on a wide range of videos show that this algorithm provides significant improvement over the state-of-the-art denoising algorithms in terms of both objective metric and subjective visual quality.

► Effective patch-based video denoising algorithm exploits local and nonlocal correlations.
► Adaptive spatio-temporal neighborhood structure is searched according to local video content.
► Similar structures are stacked together for higher nonlocal correlations.
► Patch array is transformed by SA-DCT and has sparse representation in transform domain.
► Noise is attenuated by collaborative spectrum shrinkage with iterative Wiener filtering.