  Since rainy 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. 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, resulting in sub-optimal rain
removal performance and no-equally good quality for all de-rained sub-views. In
this paper, we propose an efficient 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)
to improve the rain removal performance. To fully model the global correlation
between the spatial and the angular information, we propose an Extended
Spatial-Angular Interaction (ESAI) module to merge them, 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
real-world rain streaks and introduces colored-residue image guided contrastive
regularization to reconstruct rain-free images. Extensive experiments conducted
on synthetic and real-world LFIs demonstrate that our method outperforms the
state-of-the-art methods both quantitatively and qualitatively.
