M2-Net: Multi-stages Specular Highlight Detection and Removal in Multi-scenes
This addresses the challenge of removing unwanted specular highlights in images and videos, which is important for computer vision applications, but it appears incremental as it builds on existing highlight removal methods with a multi-stage framework.
The paper tackles the problem of specular highlight detection and removal across multiple scene types, achieving state-of-the-art results in quantitative metrics and demonstrating promising application in video highlight removal.
In this paper, we propose a novel uniformity framework for highlight detection and removal in multi-scenes, including synthetic images, face images, natural images, and text images. The framework consists of three main components, highlight feature extractor module, highlight coarse removal module, and highlight refine removal module. Firstly, the highlight feature extractor module can directly separate the highlight feature and non-highlight feature from the original highlight image. Then highlight removal image is obtained using a coarse highlight removal network. To further improve the highlight removal effect, the refined highlight removal image is finally obtained using refine highlight removal module based on contextual highlight attention mechanisms. Extensive experimental results in multiple scenes indicate that the proposed framework can obtain excellent visual effects of highlight removal and achieve state-of-the-art results in several quantitative evaluation metrics. Our algorithm is applied for the first time in video highlight removal with promising results.