EDSM: an encoder-decoder architecture face restoration network with style modulation

Yuchen Tu; Mengyao Jiang; Li Yu

doi:10.1117/12.3030002

8 May 2024 EDSM: an encoder-decoder architecture face restoration network with style modulation

Yuchen Tu, Mengyao Jiang, Li Yu

Proceedings Volume 13162, Fourth Symposium on Pattern Recognition and Applications (SPRA 2023); 131620D (2024) https://doi.org/10.1117/12.3030002
Event: Fourth Symposium on Pattern Recognition and Applications (SPRA2023), 2023, Napoli, Italy

Abstract

In recent years, face restoration methods based on deep learning with or without GAN prior have two main problems: retaining less identity information of the original input image and insufficient utilization of facial structure information. In order to solve the mentioned problems, we propose an encoder-decoder architecture face restoration network with style modulation called EDSM. First, skip connection and channel attention module are added to the basic network and a lightweight style modulation module is introduced to make full use of the global and local information extracted from the low-resolution (LR) face image. Meanwhile, identity loss is introduced to preserve identity information and a multi-scale discriminator is added to constitute the EDSM-plus network. Experiments have shown that the proposed EDSM and EDSM-plus have good face restoration performance in the Helen dataset.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuchen Tu, Mengyao Jiang, and Li Yu "EDSM: an encoder-decoder architecture face restoration network with style modulation", Proc. SPIE 13162, Fourth Symposium on Pattern Recognition and Applications (SPRA 2023), 131620D (8 May 2024); https://doi.org/10.1117/12.3030002

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KEYWORDS

Image restoration

Modulation

Lawrencium

Education and training

Network architectures

Ablation

Image resolution

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