Non-Local Means with Elliptical Window and Adaptive Parameter

In the classic non-local mean algorithm(NLM),the square search window is fixed to be square,which is highly likely to involve many image patches with low-similarity into the weighted averaging process.This way would blur the final denoised image.To this end,an elliptical search window that is defined along the local edge structure is adopted,as well as the strategy of adaptive patch size and smoothing parameter method to improve the ability of noise suppression and detail preservation.Firstly,according to the local gradient information of the image and the structure tensor,the ellipse equations along the local edge structure of the image are obtained,and the shape of the search window is therefore determined.In this way,the search window is refined to the area consistent with local structure.Then steering kernel is leveraged to obtain an ellipse image patch.Finally,the strategy of smooth parameter adaptation is also combined to further improve the performance.Compared with the traditional NLM algorithm and adaptive NLM algorithm,the experimental results show that the proposed method has higher PSNR and SSIM in the qualitative measurement,with the increase of noise level,the proposed method can suppress the noise and retain the high-frequency texture information of the image.