Towards High-Resolution Specular Highlight Detection

Gang Fu, Qing Zhang, Lei Zhu, Qifeng Lin, Yihao Wang, Siyuan Fan, Chunxia Xiao

Abstract: Specular highlight detection is an essential task with various applications in computer vision. This paper aims to detect specular highlights in single high-resolution images using deep learning while avoiding excessive GPU memory consumption. To achieve this, we present a high-resolution specular highlight detection dataset with manual annotations of specular highlights. Given our dataset, we propose a patch-level bidirectional refinement network for high-resolution specular highlight detection. The main idea is to utilize both the pathway from small-scale patch to large-scale patch and its reverse pathway to progressively refine the detection results of adjacent-scale specular highlight patches. Moreover, based on our detection network, we propose a modified inpainting framework for specular highlight removal as an application. Lastly, we provide ten potential research directions for specular highlight detection, inspiring researchers for further study.