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All Journal JRST (Jurnal Riset Sains dan Teknologi) Query : Jurnal Sistem Informasi JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Tunas Journal of Computer Networks, Architecture and High Performance Computing JiTEKH (Jurnal Ilmiah Teknologi Harapan) Prosiding Snastikom Prioritas : Jurnal Pengabdian Kepada Masyarakat Insight Power (Kekuatan Wawasan) - Jurnal Pengabdian Kepada Masyarakat Digital (JUPED) Jurnal Pengabdian Masyarakat Bangsa Jurnal Ilmu Komputer dan Sistem Informasi
Tommy Tommy
Fakultas Teknik Dan Komputer, Program Studi Teknik Informatika, Universitas Harapan Medan, Medan, Indonesia
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Reconstruction of Low-Resolution Facial Images Using Improvements to the FSRCNN Model with Parallel Feature Extraction Layers and Residual Connections Tommy Tommy; Rosyidah Siregar; Edy Rahman Syahputra
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.24237

Abstract

Enhancing the quality of reconstructing low-resolution facial images poses a significant challenge in various applications, particularly in CCTV-based surveillance. This study develops a Fast Super-Resolution Convolutional Neural Network (FSRCNN) model with the addition of parallel feature extraction layers and residual connections from these layers to the expanding layer to improve the reconstruction quality of faces from low resolution. The proposed model is tested using two datasets: CelebA and CCTV footage. The tests are conducted for two reconstruction scales, namely from LR to HR and from LR to intermediate. The results demonstrate that the proposed model consistently outperforms the standard FSRCNN. In the LR to HR reconstruction, the proposed model achieves an average PSNR improvement of 1.24 dB and an SSIM increase of 0.0584 on the CelebA dataset. For the CCTV dataset, an average PSNR enhancement of 0.52 dB and an SSIM improvement of 0.025 are attained. While this model significantly enhances image quality, several limitations were identified regarding performance on more varied CCTV images and the higher complexity of the model.
Co-Authors Abdul Fattah Hasibuan Amir Mahmud Husein, Mawaddah Harahap, Amir Andi Marwan Elhanafi Dedek Prasetyo Denny Abdul Malik Divi Handoko Dwi Resty Ananda Edy Rahman Syahputra Fera Damayanti Ferdy Riza Harahap, Herlina Hasibuan, Ade Zulkarnain Herlina Harahap Jadi Olo Pangihutan Kalvin Chiuloto Liza Novietta M. Mahdi Maulana Lubis Manovri Yeni Mawaddah Harahap Nenna Irsa Syahputri Nur Wulan Nurcholis Geofany Risko Liza Rizkiman Waruru Rizko Liza Ruswan Nurmadi Siregar, Rosyidah Syahputri, Nenna Irsa