Non-local self-similarity based image denoising depends strictly on similarity measure. The denoising performance is determined based on the ability to reliably find sufficient number of similar patches. In this paper, we propose a rotation invariant similarity measure to fully exploit the image non-local self-similarity. Instead of using image patches, we employ local frequency descriptors, that are rotation invariant and robust to noise, to measure the similarity. Thus, both translational and rotational similarity can be handled even at high noise level. The comparative experimental results show that the proposed method is effective as a rotation invariant similarity measure, and it can consistently improve the performance of non-local means algorithm to achieve better denoising results.
['Chenglin Zuo', 'Ljubomir Jovanov', 'Hiep Quang Luong', 'Bart Goossens', 'Wilfried Philips', 'Yu Liu', 'Maojun Zhang']
Rotation invariant similarity measure for non-local self-similarity based image denoising