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.