SGNet: Style-Guided Network With Temporal Compensation for Unpaired Low-Light Colonoscopy Video Enhancement.

Abstract

A low-light colonoscopy video enhancement method is needed as poor illumination in colonoscopy can hinder accurate disease diagnosis and adversely affect surgical procedures. Existing low-light video enhancement methods usually apply a frame-by-frame enhancement strategy without considering the temporal correlation between them, which often causes a flickering problem. In addition, most methods are designed for endoscopic devices with fixed imaging styles and cannot be easily adapted to different devices. In this paper, we propose a Style-Guided Network (SGNet) for unpaired Low-Light Colonoscopy Video Enhancement (LLCVE). Given that collecting content-consistent paired videos is difficult, SGNet adopts a CycleGAN-based framework to convert low-light videos to normal-light videos, in which a Temporal Compensation (TC) module and a Style Guidance (SG) module are proposed to alleviate the flickering problem and achieve flexible style transfer, respectively. The TC module compensates for a low-light frame by learning the correlated feature of its adjacent frames, thereby improving the temporal smoothness of the enhanced video. The SG module encodes the text of the imaging style and adaptively explores its intrinsic relationships with video features to obtain style representations, which are then used to guide the subsequent enhancement process. Extensive experiments on a curated database show that SGNet achieves promising performance on the LLCVE task, outperforming state-of-the-art methods in both quantitative metrics and visual quality.