联合双通道对比度和L_(0)正则化强度及梯度先验的模糊图像盲复原

Blind Restoration of Blurred Images Combining Dual-Channel Contrast,L_(0) Regularization Intensity,and Gradient Prior

双通道对比度先验(Dual-CP)是基于图像的亮通道和暗通道之间的差异来模拟对比度,故其在模糊图像盲复原中表现出良好的复原效果。但是,实际应用中图像亮通道和暗通道的值并不像理论研究的那样明显地分布在1和0上,为解决这一问题,提出一个联合双通道对比度先验和L_(0)正则化强度及梯度先验的模糊图像盲复原算法。其中,由于非凸的L_(0)极小化问题求解比较困难,利用半二次分裂法推导出一种有效优化算法。实验表明,所提算法在直观效果上有更明显的细节恢复能力,且在Levin等人、Köhler等人和Lai等人提出的基准数据集上平均峰值信噪比分别提高了2.1051 dB、1.1273 dB和0.4491 dB,平均结构相似性分别提高了0.1302、0.0599和0.0158。

Dualchannel contrast prior(DualCP)simulates contrast using the difference between the bright channel and the dark channel of an image,and it achieves good results in the blind restoration of blurred images.However,in practical applications,the values of the bright channel and the dark channel of an image are not distributed on 1 and 0 as theoretically researched.This paper proposes a blind image restoration algorithm that combines DualCP,L_(0) regularization strength,and gradient prior,wherein an effective optimization algorithm is derived using semiquadratic splitting method to solve the nonconvex L_(0) minimization problem.Experiments demonstrate that the proposed method has better intuitive description recovery capabilities,and on the benchmark dataset presented by Levin et al.,Köhler et al.,and Lai et al.,the average peak signaltonoise ratio increased by 2.1051 dB,1.1273 dB,and 0.4491 dB,respectively,and the average structural similarity increased by 0.1302,0.0599,and 0.0158,respectively.