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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika
WULANDARI, ESTI FITRIA
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Electrical & Electronics Engineering Engineering

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Enhancing Hazy Image Quality with a Modular CNN Encoder–Decoder KHOLIQ, ANDIKA MUHAMMAD NUR; SATYAWAN, ARIEF SURYADI; HAQIQI, MOKH MIRZA ETNISA; AKBAR, FAJAR RAHMAT; NURROHMAH, IASYA FAIQOH; ADAWIYAH, AULIA; WULANDARI, ESTI FITRIA; SUGIAN, RENDI TRI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 14, No 1: Published January 2026
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v14i1.69

Abstract

This study develops a modular CNN encoder–decoder framework for single-image dehazing by replacing the conventional bottleneck with interchangeable token-mixing modules such as FNet, Spatial-FNet, MLP-Mixer, and gMLP-style designs. The pipeline integrates adaptive preprocessing (CLAHE and histogram matching), photometric augmentations, and training on a controlled subset of the SOTS dataset. Comprehensive quantitative and qualitative evaluations demonstrate substantial improvements over a baseline CNN, with mean PSNR increasing from approximately 18.4 dB to the 23.0–24.0 dB range and SSIM rising from about 0.75 to roughly 0.89–0.91. However, several variants require careful hyperparameter selection and loss-weight tuning to achieve stable performance. The results offer practical guidance for deployment in real-world vision systems.
Co-Authors ADAWIYAH, AULIA AKBAR, FAJAR RAHMAT HAQIQI, MOKH MIRZA ETNISA KHOLIQ, ANDIKA MUHAMMAD NUR NURROHMAH, IASYA FAIQOH Satyawan, Arief Suryadi SUGIAN, RENDI TRI