SF-SRGAN: PROGRESSIVE GAN-BASED FACE HALLUCINATION
| Autor: | M. N. Favorskaya, A. I. Pakhirka |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-2-W3-2023, Pp 47-52 (2023) |
| Druh dokumentu: | article |
| ISSN: | 1682-1750 2194-9034 |
| DOI: | 10.5194/isprs-archives-XLVIII-2-W3-2023-47-2023 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|