针对暗通道先验时间复杂度高的问题, 提出一种多局部模糊核融合的盲去模糊算法.该算法采用并行方式分块求解局部模糊核, 利用局部模糊核的形状相似性将其融合为一个全局模糊核(点扩散函数).对于初步融合的全局模糊核上出现的噪点, 利用其邻域的情况进行关联性调整, 进一步改善融合效果.实验和统计分析结果表明, 该算法在保证去模糊效果的情况下, 有效提升了图像去模糊的速度, 在部分真实模糊图像的局部细节还原上效果更佳, 并且可以很好地处理大尺寸的模糊图像.

A multi-local kernels′ fusion algorithm is proposed to solve the problem of high complexity of dark channel priors. In this algorithm, the local kernels are computed and solved in parallel, and then merged into a global kernel (point spread function) by using the shape similarity of local kernels. For the noise that has appeared on the initially merged global kernel, the relevance adjustment is introduced using the neighboring information to further improve the fusion effect. Experiments show that the proposed algorithm can effectively improve the speed of image deblurring while guaranteeing the deblurring effect. It also has better effect on the local detail restoration of some real blurred images, and can handle large-size blurred images well.