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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JRST (Jurnal Riset Sains dan Teknologi) Query : Jurnal Sistem Informasi JURIKOM (Jurnal Riset Komputer) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JATI (Jurnal Mahasiswa Teknik Informatika) INFOKUM Jurnal Tunas Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Journal of Computer Networks, Architecture and High Performance Computing JiTEKH (Jurnal Ilmiah Teknologi Harapan) JIKEM: Jurnal Ilmu Komputer, Ekonomi dan Manajemen Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) EXPLORER Prosiding Snastikom Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Prioritas : Jurnal Pengabdian Kepada Masyarakat Insight Power (Kekuatan Wawasan) - Jurnal Pengabdian Kepada Masyarakat Digital (JUPED) Jurnal Pengabdian Masyarakat Bangsa JUDIS : Jurnal Multidisiplin dan Sains Jurnal Pengabdian Masyarakat Berdampak
Siregar, Rosyidah
Universitas Harapan Medan
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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 Ainun Naimah Pulungan Amir Mahmud Husein, Mawaddah Harahap, Amir An-nissa, Shahira Andi Marwan Elhanafi Andi Marwan Elhanafi Andi Marwan Elhanafi, Andi Marwan AULIA, RAHMAT Damayanti, Fera Edy Rahman Syahputra Edy Rahman Syahputra, Edy Rahman Fera Damayanti Ferdy Riza Gultom, Fransko Harahap, Herlina Herlina Andriani Simamora Herlina Harahap Ilham, Fauzi Imran Lubis IRWAN, DEDY Jawak, Otniel Efrata Khairani, Mufida Kharunnisa Kharunnisa Liza Novietta Lubis, Husni lubis, ihsan M F Verri Anggriawan Manovri Yeni Mawaddah Harahap Muhammad Zen Nenna Irsa Syahputri Rahman Syahputra, Edy Rahman, Sayuti Rismayanti Rismayanti Ruswan Nurmadi Sanjaya, Tedy Sembiring, Arnes Siregar, Riska Novasari Siti Sundari Syahputri, Nenna Irsa Tommy Tommy Tommy Tommy Tommy Tommy, - Tommy, Tommy Winanda, Icha Yessi Fitri Annisah Lubis Yusmai, Yusmaifany