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Sani Moch Sopian
Department of Electrical Engineering, Faculty of Engineering, Garut University, West Java, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering

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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
Teknika Vol. 14 No. 2 (2025): July 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i2.1235

Abstract

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.
Co-Authors Arief Suryadi Satyawan Beni Wijaya Firman Helfy Susilawati Khaulyca Arva Artemysia Mokhammad Mirza Etnisa Haqiqi Muhammad Ikbal Samie