Raindrop-Removal Image Translation Using Target-Mask Network with Attention Module

Abstract

Image processing plays a crucial role in improving the performance of models in various fields such as autonomous driving, surveillance cameras, and multimedia. However, capturing ideal images under favorable lighting conditions is not always feasible, particularly in challenging weather conditions such as rain, fog, or snow, which can impede object recognition. This study aims to address this issue by focusing on generating clean images by restoring raindrop-deteriorated images. Our proposed model comprises a raindrop-mask network and a raindrop-removal network. The raindrop-mask network is based on U-Net architecture, which learns the location, shape, and brightness of raindrops. The rain-removal network is a generative adversarial network based on U-Net and comprises two attention modules: the raindrop-mask module and the residual convolution block module. These modules are employed to locate raindrop areas and restore the affected regions. Multiple loss functions are utilized to enhance model performance. The image-quality assessment metrics of proposed method, such as SSIM, PSNR, CEIQ, NIQE, FID, and LPIPS scores, are 0.832, 26.165, 3.351, 2.224, 20.837, and 0.059, respectively. Comparative evaluations against state-of-the-art models demonstrate the superiority of our proposed model based on qualitative and quantitative results.