Reference-Based Image Dehazing With Internal and External Contrastive Learning
Yanting Liu, Hui Yin, Aixin Chong, Jin Wan · IEEE Transactions on Circuits and Systems for Video Technology · 2023
Collecting paired pixel-aligned hazy/haze-free image pairs in real-world is arduous for full-supervised image dehazing. Alternatively, methods employing unpaired hazy/clear images have been developed, yet their learning ability about content information of the hazy images is easily disturbed by content-independent clear images, causing artifact problems, particularly for thick hazy images. To address the above issues, we propose a new reference-based image dehazing paradigm with hazy/reference images, where the reference image is clear and taken at the same scene as the hazy image. Therefore, how to maximize the reference value from the hazy/reference images with similar content but unaligned pixels becomes a key issue. Here, we construct a reference-based contrastive learning framework to realize the effective utilization of hazy/reference image pairs. Specifically, internal contrastive learning is designed to preserve the local content invariance between the dehazed images and hazy images in a patch-wise contrastive manner, while the other external contrastive learning learns the global content consistency between the dehazed images and reference images in an overall contrastive manner. Additionally, we design a style consistency loss committee consisting of a regular adversarial loss and a style loss. The former aims to ensure each dehazed image consistent with the overall style distribution of the entire reference set, while the latter is intended to make each dehazed image have an exclusive style with the corresponding reference image. Extensive experiments corroborate that the reference-based dehazing paradigm is recommendable and reliable, and the proposed method performs admirably against other state-of-the-art methods.