Variational Bayesian Inference Deraining Network Based on Latent Variable Grouping

Recently, data-driven methods for image deraining have achieved remarkable progress. However, the scarcity of accurately paired real-world rainy and clean images poses considerable challenges, which consequently hinders the generalization capability of existing networks to real-world rainy scenarios. To tackle these problems, we propose a variational Bayesian inference deraining network (VBDNet) that integrates model-driven and data-driven approaches within a unified variational Bayesian framework. Specifically, we introduce a latent variable grouping strategy to enhance the expressive capacity of the prior and variational posterior for modelling complex rain distributions. To maintain distributional consistency across groups, a residual distribution mechanism is designed to stabilize the Kullback-Leibler divergence during optimization. Moreover, we redesign BNet with a multi-scale feature fusion network, which enables it to better model the diverse and complex structures of rain streaks. VBDNet consists of BNet for background inference, RNet for rain streak generation with grouped latent variables and DNet as a discriminator for adversarial learning. Extensive experiments on both synthetic datasets and real-world SPA-Data demonstrate that VBDNet achieves superior deraining performance and exhibits stronger generalization capability compared with state-of-the-art methods.