SphinxNet - A Lightweight Network for Single Image Deraining
Chirag Jasuja, Harshit Kumar Gupta, Devanshu Gupta, Anil Singh Parihar · 2021 International Conference on Intelligent Technologies (CONIT) · 2021
Rain produces haze and streak noise in an image that destroys the intricate details, turning even the most effective computer vision algorithms obsolete. This calls for a good deraining algorithm to restore a rainy image to a rain-less image with natural sharpness, contrast and brightness. In this paper, we propose SphinxNet with a less number of parameters by utilizing autoencoders in an intelligent formation to derive maximum spatial awareness. Which helps it to achieve not only sharp but better derained results than other models with more number of trainable parameters. Autoencoders are made of linear sequence of convolutional layers with channel wise bottleneck and skip concatenation connections between encoder and decoder layers. We trained our network on popular synthetic datasets. In quantitative and qualitative analysis we found our method to perform not only better but more consistent on variety of images.