噪声估计可应用于图像去噪、图像复原和图像质量评价等多个方面。遥感影像噪声主要表现为高斯噪声,由于其纹理复杂度高,噪声估计十分困难。针对遥感影像,提出了基于数学形态学的分层噪声估计算法。该方法首先利用不同大小的结构元素采用形态学闭运算在二值边缘图像上分层提取平稳区域,在此基础上计算不同层的噪声方差估计值;其次,分析相邻层噪声方差估计值的变化规律,根据特定准则确定噪声和信息的最佳分离层;最后,分析信噪分离层及其相邻层的特性,依据特定规则估计图像噪声值。实验结果证明,该方法与传统噪声估计方法相比,可以有效解决分块不准确问题,噪声估计误差低,能够应用于复杂纹理图像,适用范围更广。

Noise estimation can be applied to image denoising, image restoration and image quality assessment and so on. The main performance of noise for remote sensing image is Gaussian noise. Due to high complexity of its texture, it is very difficult to estimate noise. For remote sensing image, a noise estimation algorithm was proposed based on layered mathematical morphology. Firstly, stable regions of different layer were extracted by close-operation with different structuring elements in binary edge image. On this basis, noise value was estimated in different layer. Secondly, the best separation layer of noise and signal was determined by analyzing the variation of noise variance of adjacent layers. Finally, noise value was estimated by specific rules based on the properties of signal-to-noise separation layer and its adjacent layer. Experimental results show that the proposed method can effectively solve the problem of block inaccuracy compared with traditional algorithms. The noise standard deviation relative error is less than traditional algorithms. It can be used in images with complex texture and has a wider range of application.