Abstract: An algorithm combining local entropy and histogram statistics was proposed in order to solve the problem that it is difficult to detect the blurred radius quickly and correctly in defocused image blind restoration. Firstly, the gray level information of blurred image was extracted by local entropy filter and straight lines were detected by Canny edge detector and Hough transform. Secondly, the straight step edges were located to compute the line spread function adopting Grubbs method and histogram feature of the parallel line area. Finally, the blurred radius could be obtained by line spread function. The experimental results show that the step edges can be located quickly and accurately when blurred radius is small, thus increasing recognition accuracy and recognition efficiency.

Key words: defocused image, blurred radius, local entropy, histogram statistics, step edge

摘要： 针对离焦模糊图像盲复原中模糊半径难以快速精确检测的问题，提出了局部熵和直方图统计相结合的算法。首先对模糊图像进行局部熵滤波提取图像的灰度变化信息量，利用Canny边缘算子和Hough变换检测出离焦图像的直线边缘；然后利用相互平行直线边缘区域内的直方图统计特性和Grubbs检验法，定位出阶跃直线边缘求出线扩散函数；最后利用线扩散函数得到模糊半径。实验结果表明所提算法在模糊半径较小时能够精确快速地定位阶跃边缘，从而提高模糊半径的识别精度和识别效率。

关键词: 离焦图像, 模糊半径, 局部熵, 直方图统计, 阶跃边缘