Face hallucination from low quality images using definition-scalable inference

• Published in: Pattern recognition Vol. 94; pp. 110 - 121
• Main Authors: Hu, Xiao, Ma, Peirong, Mai, Zhuohao, Peng, Shaohu, Yang, Zhao, Wang, Li
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2019
• Subjects: Deep learning; Generative adversarial networks; PCA; SIFT; Sparse representation; Deep learning; Sparse representation; Generative adversarial networks; SIFT; PCA
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2019.05.027

•A method is introduced to hallucinate super-low face from real low-quality face instead of stimulated low-quality face.•A definition-scalable strategy: a face is decomposed into a basic face with low-definition and an enhanced face with high-frequency information.•The super-resolution technique based on definition-scalable inference effectively estimate structural information and high-frequency texture from real low-res faces.•The matched SIFT key-points is proposed to estimate the similarity of the super-res face and its high-res labeled face.•The proposed SISR method can recover more structure information and local information from real low-quality face and more SIFT key-points than the state of the arts. To hallucinate super-resolution (super-res) face from a real low-quality face, a super-resolution technique based on definition-scalable inference (SRDSI) is proposed in this paper. In the proposed strategy, all high-res labeled faces are first decomposed into basic faces and enhanced faces to train a basic face and an enhanced face inferring model, and then two inferring models are used to hallucinate super-res basic face with low-definition and enhanced faces with high-frequency information from a single low-res face. Finally, the basic face is merged with its enhanced face into a super-res face with high-definition. In addition, this paper employs SIFT key-points to evaluate the similarity between the super-res face and its high-res labeled face. Experimental results show that SRDSI can effectively recover more structural information as well as SIFT key-points from real low-res faces and achieves better performance than state-of-the-art super-resolution techniques in terms of both visual and objective quality. [Display omitted]