Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging

Abstract

Endoscopy is the most widely imaging technique used for the diagnosis of cancerous lesions in hollow organs. However, endoscopic images are often affected by illumination artefacts: image parts may be over- or underexposed according to the light source pose and the tissue orientation. These artifacts have a strong negative impact on the performance of computer vision or artificial intelligence based diagnosis tools. Although endoscopic image enhancement methods are greatly required, little effort has been devoted to over- and underexposure enhancement in real-time. This contribution presents an extension to the objective function of LMSPEC, a method originally introduced to enhance images from natural scenes. It is used here for the exposure correction in endoscopic imaging and the preservation of structural information. To the best of our knowledge, this contribution is the first one that addresses the enhancement of both types of endoscopic artefacts (under- and over-exposure) using deep learning methods. Tested on the Endo4IE dataset, the proposed implementation has yielded a significant improvement over LMSPEC reaching a SSIM increase of 4.40% and 4.21% for over- and under-exposed images, respectively.

Self-Supervised and Supervised Image Enhancement Networks with Time-Shift Module

“…The coarse-to-fine deep network [21] for exposure correction achieved PSNR and SSIM scores of 22.28 and 0.772 [33] on the overexposed and 23.06 and 0.760 on the underexposed images of the Endo4IE dataset. Espinosa et al [33] improved the method by adding an SSIM loss module into the model and improved the results to SSIM scores of 0.806 and 0.792 for over and underexposed images. The superior results were obtained by Mou et al [22].…”