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Aditya, Dion
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Computer Science & IT Electrical & Electronics Engineering

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A Single Image Dehazing using U-Net and Lightweight Vision Transformer Aditya, Dion; Yosrita, Efy
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2352

Abstract

This research presents a single-image dehazing method that integrates a Lightweight Vision Transformer (LVT) and U-Net to capture both local and global features. LVT enhances resolution, U-Net extracts local features, and LVT refines global dependencies before fusion. Evaluations on O-Haze and HSTS datasets show PSNR scores of 27.88 (O-Haze, ResNet-50) and 28.22 (HSTS, no backbone), outperforming existing methods while maintaining competitive SSIM. The results demonstrate effectiveness in real-world haze scenarios, such as wildfire-induced haze in Indonesia.
Co-Authors Efy Yosrita, Efy