Fast Single Image Rain Removal via a Deep Decomposition-Composition Network

Abstract

Rain effect in images typically is annoying for many multimedia and computer vision tasks. For removing rain effect from a single image, deep leaning techniques have been attracting considerable attentions. This paper designs a novel multi-task leaning architecture in an end-to-end manner to reduce the mapping range from input to output and boost the performance. Concretely, a decomposition net is built to split rain images into clean background and rain layers. Different from previous architectures, our model consists of, besides a component representing the desired clean image, an extra component for the rain layer. During the training phase, we further employ a composition structure to reproduce the input by the separated clean image and rain information for improving the quality of decomposition. Experimental results on both synthetic and real images are conducted to reveal the high-quality recovery by our design, and show its superiority over other state-of-the-art methods. Furthermore, our design is also applicable to other layer decomposition tasks like dust removal. More importantly, our method only requires about 50ms, significantly faster than the competitors, to process a testing image in VGA resolution on a GTX 1080 GPU, making it attractive for practical use.

References in corpus (3)

• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
• Image De-raining Using a Conditional Generative Adversarial Network

Cited by in corpus (4)

• An Effective Two-Branch Model-Based Deep Network for Single Image Deraining
• Gradient Information Guided Deraining with A Novel Network and Adversarial Training
• Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes
• Deep Image Deraining Via Intrinsic Rainy Image Priors and Multi-scale Auxiliary Decoding