混合鲁棒权重和改进方法噪声的两级非局部均值去噪

传统非局部均值去噪算法采用指数型函数计算相似性权重，不能准确反映图像块之间的相似性；现有两级非局部均值去噪算法对方法噪声的获取以及方法噪声中所含信息的利用不够充分。针对上述问题，提出一种混合鲁棒权重和改进方法噪声的两级非局部均值去噪算法。首先采用一种改进的混合鲁棒权重函数来计算图像块的相似性权重；再利用预去噪后的图像构造新的方法噪声，并与两级去噪框架相结合；最后将提出的混合鲁棒权重函数和改进的方法噪声应用到两级非局部均值去噪方法中。实验结果表明，该算法既能准确地反映图像块之间的相似性，也能充分利用方法噪声的信息，且在去噪性能与结构细节保持能力方面均优于传统算法。

Two-stage non-local means denoising based on hybrid robust weight and improved method noise

Traditional non local means denoising algorithm calculates similarity weight between image patches using exponential functions, which cannot accurately reflect the similarity between image patches. Method noise obtained by existing two stage non local means methods is unsatisfactory, and the information contained is insufficiently used. Aiming at the problems above, we propose a novel algorithm called two stage non local means denoising based on hybrid robust weight and improved method noise. Firstly, we propose to use an enhanced hybrid robust weight function to calculate the similarity between image patches. Secondly, we use the pre denoised image to construct improved method noise, which is then combined with a two stage framework. Finally, the new hybrid robust weight function as well as the improved method noise is applied to the two stage non local means scheme. Experimental results show that the proposed algorithm can calculate the similarity between image patches more precisely and make the best of method noise, and it has better performance in denoising and preserving structure details than traditional ones.