基于梯度分组的NL-means改进型SAR图像降噪算法

非局部平均降噪（non-local means, NL-means）算法是近期提出的针对高斯噪声的降噪算法，能够有效地保持图像纹理，但是其计算量庞大，而且要求噪声符合高斯分布，这限制了其在合成孔径雷达（synthetic aperture radar， SAR）图像上的应用。利用梯度分组的相似点匹配算法对NL-means算法进行改进，在降低计算量的同时进一步提高降噪质量。针对SAR乘性噪声特点，引入同态变换处理使改进后的算法能够适用于SAR图像降噪。通过仿真实验对本算法进行验证，降噪处理的峰值信噪比比同类算法平均提高3 dB，执行速度比NL-means约提高了3倍。

Improved NL-means algorithm based on gradient grouping for SAR image despeckling

Recently, a non-local means (NL-means) algorithm is proposed for the denoising of Gaussian noises, and it can effectively preserve textures while removing noises. However, the computation of NL-means is extremely heavy, and furthermore, the Gaussianity of the noise is required. Due to these limitations, NL-means is not suitable for synthetic aperture radar （SAR） image despeckling. A similar points matching method based upon gradient grouping is presented, and this method leads to an improved NL-means model. Compared with the existing NL-means, the improved model can achieve a better despeckling performance with lower computation. On the other hand, since SAR image is with multiplicative noise, the holomorphic transform is introduced as a pre-processing to cater for SAR image despcekling. Experimental results demonstrate that the proposed method achieves 3 dB of PSNR gain in comparison with the existing relevant methods and the computation is around 3 times faster than that of the NL-means.