Weakly Supervised Specular Highlight Removal Using Only Highlight Images

Weakly Supervised Specular Highlight Removal Using Only Highlight Images

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Specular highlight removal is a challenging task in the field of image enhancement, while it can significantly improve the quality of image in highlight regions.Recently, deep learning-based methods have been widely adopted in this task, demonstrating excellent performance by training on either massive paired data, wherein both the highlighted and highlight-free versions of the same image are available, or unpaired datasets where the one-to-one correspondence is inapplicable.However, it is difficult to obtain the corresponding highlight-free Catchers version of a highlight image, as the latter has already been produced under specific lighting conditions.In this paper, we propose a method for weakly supervised specular highlight removal that only requires highlight images.This method involves generating highlight-free images from highlight images with the guidance of masks estimated using non-negative matrix factorization (NMF).

These highlight-free images are then fed consecutively into a series of modules derived from a Cycle Generative Adversarial Network (Cycle-GAN)-style network, namely the highlight generation, highlight removal, and reconstruction modules in sequential order.These modules are trained jointly, resulting in a highly effective highlight removal module during the verification.On the specular highlight image quadruples (SHIQ) and the LIME datasets, our method achieves an accuracy of 0.90 and a balance error rate (BER) of 8.6 on SHIQ, and an accuracy of 0.

89 and a BER of 9.1 on LIME, outperforming existing methods and demonstrating its potential Accessories - Other - Health and Wellness for improving image quality in various applications.