School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China.
Sensors (Basel). 2019 Jan 16;19(2):339. doi: 10.3390/s19020339.
Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks can be selected with several biggest LGSE values in a descending order. Then, the Haar wavelet-based local median absolute deviation (HLMAD) is presented to compute the local variance of these selected homogenous blocks. After that, the noise parameters can be estimated accurately by applying the maximum likelihood estimation (MLE) to analyze the local mean and variance of selected blocks. Extensive experiments on synthesized noised images are induced and the experimental results show that the proposed method could not only more accurately estimate the noise of various scene images with different noise levels than the compared state-of-the-art methods, but also promote the performance of the blind de-noising algorithm.
图像传感器的噪声估计是许多图像预处理应用中的关键技术，例如盲去噪。现有的加性白高斯噪声（AWGN）和泊松-高斯噪声（PGN）的噪声估计方法在纹理较重的场景图像情况下可能会低估或高估噪声水平。针对这个问题，本文提出了一种新的基于均匀块的噪声估计方法来计算这些噪声。首先，将有噪声的图像转换为局部灰度统计熵（LGSE）图，并按降序排列选择几个最大 LGSE 值的弱纹理图像块。然后，提出基于 Haar 小波的局部中值绝对偏差（HLMAD）来计算这些选择的均匀块的局部方差。之后，通过应用最大似然估计（MLE）来分析所选块的局部均值和方差，可以准确估计噪声参数。在合成噪声图像上进行了广泛的实验，实验结果表明，与现有的先进方法相比，该方法不仅可以更准确地估计不同噪声水平和不同场景图像的噪声，而且可以提高盲去噪算法的性能。
