May 9, 2024, 4:45 a.m. | Zhilei Liu, Chenggong Zhang

cs.CV updates on

arXiv:2405.04778v1 Announce Type: cross
Abstract: Traditional face super-resolution (FSR) methods trained on synthetic datasets usually have poor generalization ability for real-world face images. Recent work has utilized complex degradation models or training networks to simulate the real degradation process, but this limits the performance of these methods due to the domain differences that still exist between the generated low-resolution images and the real low-resolution images. Moreover, because of the existence of a domain gap, the semantic feature information of the …

abstract arxiv datasets edge eess.iv embedding face fsr images information network networks performance process resolution synthetic training type work world

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