Image noise level estimation based on affine reconstruction and noise sample histogram
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摘要: 结合信号仿射重建技术和图像噪声散点直方图，提出了一种图像噪声水平估计方法。首先，对于输入的噪声图像，采用基于分水岭的图像分割算法，将其分为若干像素均匀的图像块。采用仿射信号重建算法，实现无噪声的仿射图像信号和噪声余量图的分离和获取。从噪声余量图中计算获取各图像分块的噪声散粒点，每个散粒点表示各个图块的噪声标准差大小。随后，统计噪声散粒点直方图，进而确定最多散粒点分布的噪声强度区间。最终的图像噪声标准差估计值由该选择区间内的所有散粒点标准差均值计算得到。对比实验表明，算法能够进行准确可靠的图像噪声水平估计，对于细节和边缘丰富的图像效果优异。Abstract: An image noise level estimation method was presented by using affine reconstruction technique and the calculated noise sample histogram. The watershed-based image segmentation was firstly utilized to divide the noisy image into several homogenous blocks. Then by applying affine reconstruction technique, the noiseless affine image signal and the noise residual image were obtained. Noise samples for the standard deviation values of each segmented patch were calculated from the noise residual image. After that the histogram of estimated noise samples was described to find out the specific noise level interval with the most noise samples falling into. Finally, the image noise standard deviation was computed by the average of noise samples in the selected noise interval. Experiments are implemented to demonstrate the effectiveness of the proposed algorithm. The presented method could produce accurate and reliable estimation results for images with rich textures and edges.
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