  Recent CNN-based methods for image deraining have achieved excellent
performance in terms of reconstruction error as well as visual quality.
However, these methods are limited in the sense that they can be trained only
on fully labeled data. Due to various challenges in obtaining real world
fully-labeled image deraining datasets, existing methods are trained only on
synthetically generated data and hence, generalize poorly to real-world images.
The use of real-world data in training image deraining networks is relatively
less explored in the literature. We propose a Gaussian Process-based
semi-supervised learning framework which enables the network in learning to
derain using synthetic dataset while generalizing better using unlabeled
real-world images. More specifically, we model the latent space vectors of
unlabeled data using Gaussian Processes, which is then used to compute
pseudo-ground-truth for supervising the network on unlabeled data. Through
extensive experiments and ablations on several challenging datasets (such as
Rain800, Rain200L and DDN-SIRR), we show that the proposed method is able to
effectively leverage unlabeled data thereby resulting in significantly better
performance as compared to labeled-only training. Additionally, we demonstrate
that using unlabeled real-world images in the proposed GP-based framework
results
