Specular Highlight Detection and Removal Based on Dynamic Association Learning

Specular highlight widely exists in daily life. Its strong brightness influences the recognition of text and graphic patterns in images, especially for documents and cards. In this paper, we propose a coarse-to-fine dynamic association learning method for specular highlight detection and removal. Specifically, based on the dichromatic reflection model, we first use a sub-network to separate the specular highlight layer and locate the regions of the highlight. Instead of directly subtracting the estimated specular highlight component from the raw image to get the highlight removal result, we design an associated learning module (ALM) together with a second-stage sub-network to restore the color distortion of the specular highlight layer removal. Our ALM respectively extracts features from the specular highlight part and non-specular highlight part to improve the color restoration. We conducted extensive evaluation experiments and the ablation study on the synthetic dataset and the real-world dataset. Our method achieved 36.09 PSNR and 97% SSIM on SHIQ dataset, along with 28.90 PSNR and 94% SSIM on SD1 dataset, which outperformed the SOTA methods.