Face Super Resolution Based on Identity Preserving V-Network

Ali Hüsameddin Ateş; Hüseyin Eski

doi:10.35377/saucis.8.91064.1525350

EN

Face Super Resolution Based on Identity Preserving V-Network

Abstract

Numerous super-resolution methods have been developed to restore and upsample low-resolution and low-detail images to higher resolutions. Specifically, face super-resolution studies aim to restore various degradations in facial images while enhancing their resolution and preserving details. This study proposes the VNet architecture, which consists of a deep learning-based convolutional network for converting low-resolution and degraded facial images into high-quality and detailed images, and a pre-trained FaceNet model to preserve identity information. The architecture leverages the advantages of the Encoder-Decoder structure bidirectionally to maintain details and recover lost information. In the initial stage, the Encoder module compresses the image representation, filtering out unnecessary information. The Decoder module then reconstructs the high-resolution and restored image from the compressed representation. The use of residual connections in this process helps minimize information loss while preserving details. The final stage utilizes the identity feedback from the FaceNet model to enhance the image without deviating from the original identity context. Tests conducted on various facial datasets demonstrate that VNet achieves high metric performance in both super-resolution and restoration tasks. The results indicate that the proposed architecture is effective in producing realistic and high-quality versions of low-resolution and degraded facial images.

Keywords

References

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Ali Hüsameddin Ateş ^*
0000-0001-7690-7301
Türkiye

Hüseyin Eski
0000-0002-6006-3228
Türkiye

Early Pub Date

March 27, 2025

Publication Date

March 28, 2025

Submission Date

July 31, 2024

Acceptance Date

March 20, 2025

Published in Issue

Year 2025 Volume: 8 Number: 1

DOI

https://doi.org/10.35377/saucis.8.91064.1525350

IZ

https://izlik.org/JA24FE69TE

Cite

RIS / Bibtex

APA

Ateş, A. H., & Eski, H. (2025). Face Super Resolution Based on Identity Preserving V-Network. Sakarya University Journal of Computer and Information Sciences, 8(1), 27-37. https://doi.org/10.35377/saucis.8.91064.1525350

AMA

1.Ateş AH, Eski H. Face Super Resolution Based on Identity Preserving V-Network. SAUCIS. 2025;8(1):27-37. doi:10.35377/saucis.8.91064.1525350

Chicago

Ateş, Ali Hüsameddin, and Hüseyin Eski. 2025. “Face Super Resolution Based on Identity Preserving V-Network”. Sakarya University Journal of Computer and Information Sciences 8 (1): 27-37. https://doi.org/10.35377/saucis.8.91064.1525350.

EndNote

Ateş AH, Eski H (March 1, 2025) Face Super Resolution Based on Identity Preserving V-Network. Sakarya University Journal of Computer and Information Sciences 8 1 27–37.

IEEE

[1]A. H. Ateş and H. Eski, “Face Super Resolution Based on Identity Preserving V-Network”, SAUCIS, vol. 8, no. 1, pp. 27–37, Mar. 2025, doi: 10.35377/saucis.8.91064.1525350.

ISNAD

Ateş, Ali Hüsameddin - Eski, Hüseyin. “Face Super Resolution Based on Identity Preserving V-Network”. Sakarya University Journal of Computer and Information Sciences 8/1 (March 1, 2025): 27-37. https://doi.org/10.35377/saucis.8.91064.1525350.

JAMA

1.Ateş AH, Eski H. Face Super Resolution Based on Identity Preserving V-Network. SAUCIS. 2025;8:27–37.

MLA

Ateş, Ali Hüsameddin, and Hüseyin Eski. “Face Super Resolution Based on Identity Preserving V-Network”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 1, Mar. 2025, pp. 27-37, doi:10.35377/saucis.8.91064.1525350.

Vancouver

1.Ali Hüsameddin Ateş, Hüseyin Eski. Face Super Resolution Based on Identity Preserving V-Network. SAUCIS. 2025 Mar. 1;8(1):27-3. doi:10.35377/saucis.8.91064.1525350