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All Journal Journal of Information Systems and Informatics Jurnal Elektronika dan Telekomunikasi
Julian Supardi
Sriwijaya University
Computer Science & IT

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Reinforcement Learning–Guided Hyperparameter Tuning for U-Net-Based Super-Resolution of Brain MRI Under Synthetic Degradation Suci Ramadini; Julian Supardi
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1565

Abstract

Low-resolution magnetic resonance imaging (MRI) may reduce visibility of fine anatomical details, motivating computational super-resolution (SR) to enhance perceived image quality. This study proposes an SR pipeline for 2D brain MRI images using a U‑Net baseline model and a reinforcement learning (RL) agent to automate hyperparameter tuning. Because the selected public dataset does not provide paired low-resolution/high-resolution (LR–HR) images, LR inputs are generated synthetically using a controlled degradation process (blur–downsample–upsample–noise), with deterministic degradation for validation and testing to ensure stable evaluation. The baseline U‑Net is trained using an L1 objective (optionally mixed with differentiable SSIM loss), AdamW optimizer, and ReduceLROnPlateau scheduler guided by validation PSNR. A Double Deep Q‑Network (Double DQN) agent then selects discrete action combinations of learning rate and SSIM-weighted loss mixing to fine-tune the baseline. For the held-out test set (n=60), the baseline improves degraded inputs from 27.04±3.21 dB to 30.10±3.59 dB PSNR and from 0.706±0.132 to 0.875±0.064 SSIM, respectively. RL fine-tuning yields a modest additional PSNR gain to 30.20±3.58 dB and SSIM remains comparable at 0.873±0.066. The paired statistical tests confirm that the PSNR improvement is significant (p<0.01), while changes in SSIM are not statistically significant, suggesting that for the tested synthetic degradation setting RL can provide reliable but incremental refinement when the baseline is already strong.
Enhancing Remote Sensing Image Resolution Using Convolutional Neural Networks Julian Supardi; Samsuryadi Samsuryadi; Hadipurnawan Satria; Philip Alger M. Serrano; Arnelawati Arnelawati
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.653

Abstract

Remote sensing imagery is a very interesting topic for researchers, especially in the fields of image and pattern recognition. Remote sensing images differ from ordinary images taken with conventional cameras. Remote sensing images are captured from satellite photos taken far above the Earth's surface. As a result, objects in satellite images appear small and have low resolution when enlarged. This condition makes it difficult to detect and recognize objects in remote-sensing images. However, detecting and recognizing objects in these images is crucial for various aspects of human life. This paper aims to address the problem of remote sensing image quality. The method used is a convolutional neural network. The results show the proposed method can improve PSNR and SSIM compared to previous methods
Co-Authors Arnelawati Arnelawati Hadipurnawan Satria Philip Alger M. Serrano Samsuryadi Samsuryadi Suci Ramadini