Image reconstruction for rain removal in both wavelet and spatial frequency sub-bands using W-CycleGANs

Tang, L. M. , Lim, L. H. I. and Siebert, P. (2023) Image reconstruction for rain removal in both wavelet and spatial frequency sub-bands using W-CycleGANs. In: Advances in Automation, Mechanical and Design Engineering. Series: Mechanisms and machine science. Springer: Cham, pp. 175-191. ISBN 9783031400728 (doi: 10.1007/978-3-031-40070-4_14)

Abstract

Although many techniques are devised in rain removal research recently, using either the Convolutional Neural Network (CNN) [1] or the Generative Adversarial Network (GAN) [2], they always require both rain and no-rain training images exist in pairs to train their networks. Recently, the Cycle-Consistent Adversarial Networks (CycleGAN) [3] has demonstrated successful results in removing real rain distortion, without the no-rain training images [4]. Building on this success, we propose a new technique called the Wavelet-CycleGANs (W-CycleGANs) that has the same advantage. In addition, the wavelet properties can be used for the CycleGAN to remove rain from images at their frequency sub-bands, in the Hue, Saturation and Value (HSV) color space [5]. We train and compare the W-CycleGANs’ to the CycleGAN’s performance fairly, using the same set of rain images as the CycleGAN [4]. Their quantitative results are compared using the Natural Image Quality Evaluator (NIQE) [6] as real rain images in pairs simply do not exist in the real world. In addition, their qualitative results are compared using visual check at zoomed-in regions. Both results have demonstrated the W-CycleGANs’ superiority in removing real rain distortions.

• Item Type: Book Sections
• Status: Published
• Glasgow Author(s) Enlighten ID: Tang, Dr Lai Meng and Siebert, Dr Paul and Lim, Dr Li Hong Idris
• Authors: Tang, L. M., Lim, L. H. I., and Siebert, P.
• College/School: College of Science and Engineering > School of Computing Science; College of Science and Engineering > School of Engineering
• Publisher: Springer
• ISBN: 9783031400728