The goal of block matching (BM) is to locate small patches of an image that are similar to a given patch or template. This can be done either in the spatial domain or, more efficiently, in a transform domain. Full search (FS) BM is an accurate, but computationally expensive procedure. Recently introduced orthogonal Haar transform (OHT)-based BM method significantly reduces the computational complexity of FS method. However, it cannot be used in applications where the patch size is not a power of two. In this paper, we generalize OHT-based BM to an arbitrary patch size, introducing a new BM algorithm based on a 2D orthonormal tree-structured Haar transform (OTSHT). Basis images of OHT are uniquely determined from the full balanced binary tree, whereas various OTSHTs can be constructed from any binary tree. Computational complexity of BM depends on a specific design of OTSHT. We compare BM based on OTSHTs to FS and OHT (for restricted patch sizes) within the framework of image denoising, using WNNM as a denoiser. Experimental results on eight grayscale test images corrupted by additive white Gaussian noise with five noise levels demonstrate that WNNM with OTSHT-based BM outperforms other methods both computationally and qualitatively.
Two-Dimensional Orthonormal Tree-Structured Haar Transform for Fast Block Matching
