High-resolution image de-raining using conditional GAN with sub-pixel upscaling

Prasen Kumar Sharma, Sathisha Basavaraju, Arijit Sur

Abstract: High-quality image de-raining is a challenging task that has been given considerable importance in recent times. To begin with, this problem is modeled as an image decomposition task where a rainy image is decomposed into the rain-free background and the associated rain streak map. Most of the existing methods have been successful in removing the rain-streaks but fails to restore the image quality, which is degraded due to noise removal. This paper proposes a novel architecture called High-Resolution Image De-Raining using Conditional Generative Adversarial Networks (HRID-GAN) to generate a de-rained image with minimal artifacts and better visual quality. Extensive experiments on publicly available synthetic as well as real-world datasets show a substantial improvement over the state-of-the-art methods SPANet (Wang et al. 2019) by ∼ 2.43% in PSNR and, DID-MDN (Zhang and Patel 2018) by ∼ 2.43%, ∼ 10.12% and ID-CGAN (Zhang et al. 2017) by ∼ 11.80%, ∼ 34.70% in SSIM and PSNR respectively.