Image denoising by block-matching and 1D filtering

In this paper, we develop a new image denoising method based on block-matching and transform-domain filtering. The developed method is derived from the current state-of-the-art denoising method (BM3D). We separate the 3D transform in the original method to two steps 1D transform, to further enhance the sparsity for signals whose elements are highly similar and to weaken the sparsity for those signals whose elements are dissimilar. Because the 1D filtering is on highly similar elements and the 2D filtering on image blocks are all removed, the image details can be better reserved and fewer artifacts are introduced than original method. Experimental results demonstrate that the developed method is competitive and better than some of the current state-of-the-art denoising methods in terms of peak signal-to-noise ratio, structural similarity, and subjective visual quality.