Colorectal endoscopic image enhancement via unsupervised deep learning

Currently, various deep learning methods have been developed to address the image enhancement tasks based on paired high-quality images as references. For the low-light endoscopic image enhancement task, it is difficult to obtain paired high-quality images and to extract features from dark areas. In addition, the enhanced images easily appear color distortions. In this study, we propose an unsupervised deep learning scheme based on the Cycle Generative Adversarial Network to enhance the endoscopic image. Because extracting features in the dark areas is important but challenging, we embedded an adaptive reverse attention module in generators to help the network focus on low-light areas and enhance these areas. We also introduce a color consistency constraint to maintain color constancy. To evaluate the performance of the proposed enhancement method, a blind evaluation methodology is proposed in view of no specific quality assessment metric specially designed on this field. Extensive subjective and objective experiment results demonstrate that the proposed method is competent for the colorectal endoscopic image enhancement task, and performs better than both conventional methods and popular deep learning-based methods on 200 real-captured colonoscopy images. In the objective experiment, the proposed method ranks first with a PIQE score of 11.1525 and an NIQE score of 11.1525, outperforming five competing methods. It also receives the best results from an average score of 1.455 over 200 test images of the subjective experiment.