TRG-Net: An Interpretable and Controllable Rain Generator

arXiv:2403.09993v1 Announce Type: new
Abstract: Exploring and modeling rain generation mechanism is critical for augmenting paired data to ease training of rainy image processing models. Against this task, this study proposes a novel deep learning based rain generator, which fully takes the physical generation mechanism underlying rains into consideration and well encodes the learning of the fundamental rain factors (i.e., shape, orientation, length, width and sparsity) explicitly into the deep network. Its significance lies in that the generator not only …

abstract arxiv cs.cv data deep learning eess.iv generator image image processing modeling novel processing rain study training type