Improved nonlocal means method based on adaptive pre-classification for image denoising

Nonlocal Means is an effective denoising method, which takes advantage of the fact that natural image has self-similarity. However, the original nonlocal means may not find enough similar candidates for some non-repetitive image blocks. In order to mitigate these drawbacks, we propose an improved nonlocal means method using adaptive pre-classification in this paper. The proposed method employs the threshold-based clustering algorithm to classify noisy image blocks adaptively. Then, a rotational block matching method is adopted to find the appropriate distance measurement between two blocks in an image. Experimental results on a set of well-known standard images show that the proposed method is effective, especially when the image contains large amount of noise.