SF-SRGAN: PROGRESSIVE GAN-BASED FACE HALLUCINATION

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Abstract. Facial hallucination is a technique that has emerged recently thanks to advances in deep learning. It can be used in various tasks such as face recognition in the wild, human identification, pedestrian re-identification, face analysis, and so on. We propose a wavelet-integrated trained face hallucination model to synthesize photorealistic face images called SF-SRGAN. The multi-stage progressive hallucination strategy is based on GAN architecture. The proposed generator consists of sequential cascade modules, each of which increases the scale by 2×. Each module has a complex structure of two branches: a progressive face hallucination branch for feature extraction and reconstruction and edge-preserving branch for high frequency detail extraction. The main difference from other progressive GAN-based face hallucination networks is that the two branches fuse followed by each cascade 2×. The model is trained and tested on popular public face datasets such as the CelebA-HQ dataset, the LFW dataset, and the Helen dataset with promising photorealistic results.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: SF-SRGAN: PROGRESSIVE GAN-BASED FACE HALLUCINATION ; volume:XLVIII-2/W3-2023 ; year:2023 ; pages:47-52 ; extent:6
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-2/W3-2023 (2023), 47-52 (gesamt 6)

Creator: Favorskaya, M. N.
Pakhirka, A. I.

DOI: 10.5194/isprs-archives-XLVIII-2-W3-2023-47-2023

URN: urn:nbn:de:101:1-2023051804292255230768

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:47 AM CEST

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Favorskaya, M. N.
Pakhirka, A. I.

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SF-SRGAN: PROGRESSIVE GAN-BASED FACE HALLUCINATION

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