基于卷积分析稀疏表示和相位一致性的低照度图像增强

针对低照度图像存在的对比度低、视觉效果差等问题，提出一种基于卷积分析稀疏表示和相位一致性的低照度图像增强方法.该方法基于Retinex模型，在估计照度图像时采用卷积分析稀疏表示进行约束，所用滤波器一部分人工设定，一部分由样本训练自动获得；在计算反射图像时利用单演相位一致性特征，施加相位一致性残余最小约束来恢复细节；通过联合约束并进行优化，得到的反射图像即为最终的增强结果.对大量低照度图像进行实验，并与当前先进方法相比，结果表明，本文方法不仅提高了图像的亮度与对比度，增强了细节，而且在多个客观评价指标上都优于其他方法.

Low-light Image Enhancement Based on Convolutional Analysis Sparse Representation and Phase Congruency

Low-light images suffer from low visibility and poor visual quality.To improve the quality of low-light images,a method based on convolution analysis sparse representation and phase congruency was proposed.This method is based on the Retinex model and improves the problem of insufficient constraints.More concretely,we used the convolutional analysis sparse representation whose filters were hand-crafted and learned from the input to estimate the illumination image.Then,by using the monogenic phase congruency,the reflection image was calculated via the phase congruency residual minimization to enhance weak details.Through joint constraints and optimization,the resulting reflection image served as the final enhancement result.Experiments on a number of challenging low-light images are presented to reveal the efficacy of our method and show its superiority over several state-of-the-arts on both subjective and objective assessments.