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Existing face super-resolution (SR) methods mainly assume the input image tobe noise-free. Their performance degrades drastically when applied toreal-world scenarios where the input image is always contaminated by noise. Inthis paper, we propose a Facial Attribute Capsules Network (FACN) to deal withthe problem of high-scale super-resolution of noisy face image. Capsule is agroup of neurons whose activity vector models different properties of the sameentity. Inspired by the concept of capsule, we propose an integratedrepresentation model of facial information, which named Facial AttributeCapsule (FAC). In the SR processing, we first generated a group of FACs fromthe input LR face, and then reconstructed the HR face from this group of FACs.Aiming to effectively improve the robustness of FAC to noise, we generate FACin semantic, probabilistic and facial attributes manners by means of integratedlearning strategy. Each FAC can be divided into two sub-capsules: SemanticCapsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facialattribute in detail from two aspects of semantic representation and probabilitydistribution. The group of FACs model an image as a combination of facialattribute information in the semantic space and probabilistic space by anattribute-disentangling way. The diverse FACs could better combine the faceprior information to generate the face images with fine-grained semanticattributes. Extensive benchmark experiments show that our method achievessuperior hallucination results and outperforms state-of-the-art for very lowresolution (LR) noise face image super resolution.

Facial Attribute Capsules for Noise Face Super Resolution