MDeRainNet: An Efficient Macro-pixel Image Rain Removal Network

Since raining weather always degrades image quality and poses significant challenges to most computer vision-based intelligent systems, image de-raining has been a hot research topic in computer vision community. Fortunately, in a rainy Light Field (LF) image, background obscured by rain streaks in one sub-view may be visible in the other sub-views, and implicit depth information and recorded 4D structural information may benefit rain streak detection and removal. However, existing LF image rain removal methods either do not fully exploit the global correlations of 4D LF data or only utilize partial sub-views (i.e., under-utilization of the rich angular information), resulting in sub-optimal rain removal performance and no-equally good quality for all de-rained sub-views. In this article, we propose an efficient neural network, called MDeRainNet , for rain streak removal from LF images. The proposed network adopts a multi-scale encoder–decoder architecture, which directly works on Macro-pixel Images (MPIs) for improving the rain removal performance. To fully model the global correlation between the spatial information and the angular information, we propose an Extended Spatial-angular Interaction (ESAI) module to merge the two types of information, in which a simple and effective Transformer-based Spatial-angular Interaction Attention (SAIA) block is also proposed for modeling long-range geometric correlations and making full use of the angular information. Furthermore, to improve the generalization performance of our network on real-world rainy scenes, we propose a novel semi-supervised learning framework for our MDeRainNet , which utilizes multi-level KL loss to bridge the domain gap between features of synthetic and that of real-world rain streaks and introduces colored-residue image-guided contrastive regularization to reconstruct rain-free images. Extensive experiments conducted on both synthetic and real-world Light Field Images (LFIs) demonstrate that our method outperforms the state-of-the-art methods both quantitatively and qualitatively.