LIIS: Low-light image instance segmentation

Abstract

Image features in low-light scenes become hard to distinguish and full of noise, which makes the performance of current popular instance segmentation models drastically degraded. We propose a two-stage approach for instance segmentation of low-light images with enhancement followed by segmentation. Stage-I corresponds to the Low-Light Image Enhancement (LLIE) process. We propose a post-processing Detail Enhancement Denoising Module (DEDM) to suppress degradation effects caused by the enhancement in the preprocessing stage. Stage-II represents the segmentation process of enhanced images. We construct the W-BCNet instance segmentation network and design a Wavelet Feature Fusion Module (WFFM) in the feature extraction stage to preserve more fine-grained features. We achieve great segmentation results on LIS, detailed comparative experiments and ablation studies show the advantages and excellent generalization ability of our model.