Lightweight Pyramid Networks for Image Deraining
This work addresses the need for efficient image deraining models suitable for resource-constrained applications like mobile devices, though it is incremental as it builds on existing pyramid decomposition techniques.
The paper tackles the problem of high parameter counts in deep convolutional neural networks for image deraining by proposing a lightweight pyramid network (LPNet) that uses Gaussian-Laplacian pyramid decomposition to simplify learning, achieving state-of-the-art performance with less than 8K parameters.
Existing deep convolutional neural networks have found major success in image deraining, but at the expense of an enormous number of parameters. This limits their potential application, for example in mobile devices. In this paper, we propose a lightweight pyramid of networks (LPNet) for single image deraining. Instead of designing a complex network structures, we use domain-specific knowledge to simplify the learning process. Specifically, we find that by introducing the mature Gaussian-Laplacian image pyramid decomposition technology to the neural network, the learning problem at each pyramid level is greatly simplified and can be handled by a relatively shallow network with few parameters. We adopt recursive and residual network structures to build the proposed LPNet, which has less than 8K parameters while still achieving state-of-the-art performance on rain removal. We also discuss the potential value of LPNet for other low- and high-level vision tasks.