Development of a Modified CycleGAN Model with Residual Blocks and Perceptual Loss for Image Dehazing - Sani Moch Sopian; Arief Suryadi Satyawan; Mokhammad Mirza Etnisa Haqiqi; Helfy Susilawati; Beni Wijaya; Khaulyca Arva Artemysia; Firman; Muhammad Ikbal Samie (Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya, 2025)
Fog reduces image contrast and clarity, creating challenges for applications such as autonomous driving and remote sensing. This study proposes a series of CycleGAN modifications for single image dehazing using unpaired data, integrating residual blocks, attention mechanisms, VGG19-based perceptual loss, and haze-aware loss. Among ten architectural variants, Modification 10 combining perceptual and haze-aware loss achieved the best overall performance. Quantitatively, it showed stable generator losses (0.91 for Gen G, 0.57 for Gen F), with improved discriminator performance (Disc X: 0.59, Disc Y: 0.47), indicating better training stability and image realism. Additionally, it offered competitive PSNR (7.99), strong SSIM (0.4202), and low LPIPS (0.6577), confirming its effectiveness in both pixel-level accuracy and perceptual quality. Qualitatively, this model generated clearer, more natural images with improved edge sharpness and detail preservation. These findings demonstrate that the modified CycleGAN significantly enhances dehazing performance and presents a valuable contribution to deep learning-based image restoration.