Semi-Supervised Image Deraining using Gaussian Processes
This work addresses the challenge of limited real-world labeled data for image deraining, offering a semi-supervised approach that improves generalization, though it is incremental as it builds on existing CNN methods.
The paper tackles the problem of poor generalization of CNN-based image deraining methods to real-world images due to reliance on synthetic training data, proposing a Gaussian Process-based semi-supervised learning framework that leverages unlabeled real-world images to achieve significantly better performance compared to labeled-only training.
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