Image rain removal and illumination enhancement done in one go
Wan, Yecong (China University of Petroleum. School of computer science and technology)
Cheng, Yuanshuo (China University of Petroleum. School of computer science and technology)
Shao, Ming-Wen (China University of Petroleum. School of computer science and technology)
Gonzàlez, Jordi (Universitat Autònoma de Barcelona)

Data:	2022
Resum:	Rain removal plays an important role in the restoration of degraded images. Recently, CNN-based methods have achieved remarkable success. However, these approaches neglect that the appearance of real-world rain is often accompanied by low light conditions, which will further degrade the image quality, thereby hindering the restoration mission. Therefore, it is very indispensable to jointly remove the rain and enhance illumination for real-world rain image restoration. To this end, we proposed a novel spatially-adaptive network, dubbed SANet, which can remove the rain and enhance illumination in one go with the guidance of degradation mask. Meanwhile, to fully utilize negative samples, a contrastive loss is proposed to preserve more natural textures and consistent illumination. In addition, we present a new synthetic dataset, named DarkRain, to boost the development of rain image restoration algorithms in practical scenarios. DarkRain not only contains different degrees of rain, but also considers different lighting conditions, and more realistically simulates real-world rainfall scenarios. SANet is extensively evaluated on the proposed dataset and attains new state-of-the-art performance against other combining methods. Moreover, after a simple transformation, our SANet surpasses existing the state-of-the-art algorithms in both rain removal and low-light image enhancement.
Ajuts:	Agencia Estatal de Investigación PID2020-120311RB-I00
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Matèria:	Contrastive learning ; Low-light image enhancement ; Rain removal ; Spatially-adaptive network
Publicat a:	Knowledge-Based Systems, Vol. 252 (September 2022) , art. 109244, ISSN 0950-7051

DOI: 10.1016/j.knosys.2022.109244

Postprint 13 p, 1.5 MB

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