Low-Light Light-Field Image Enhancement With Geometry Consistency

Deyang Liu, Zhengqu Li, Xin Zheng, Jian Ma, Yuming Fang

Abstract: Low-light Light Field (LF) enhancement aims to recover high-quality LF images from their corresponding low-quality counterparts. Although some progresses has been made, the effective utilization of LF geometry consistency for efficient low-light LF enhancement remains a challenge. Most existing methods do not adequately leverage LF geometry information during the light-up process of low-light LF, leading to a significant performance drop. To relieve this issue, we propose a low-light LF enhancement method by fully considering the geometry consistency. By introducing a spatial-epipolar geometry information interaction model, our method is able to enhance the low-light LF quality by effectively aggregating spatial information and epipolar geometry information from LF images. Moreover, to further generate higher-quality enhancements for low-light images, we design a three-stage network to enhance the fine detail information. Experimental results reveal that our method demonstrates superior low-light light field enhancement capabilities compared to previous approaches.

External IDs:dblp:conf/prcv/LiuLZMF24