Iterative attribute augmentation network for face image super resolution

Electronics Letters

Zi Teng¹  Xiaosheng Yu¹  Chengdong Wu¹ 

[1] College of Robot Science and Engineering Northeastern University Shenyang People's Republic of China;

关键词: Optical, image and video signal processing;    Image recognition;    Computer vision and image processing techniques;   

DOI  :  10.1049/ell2.12285

来源: DOAJ

【 摘 要 】

Abstract Although recently a series of deep‐learning based methods have promoted the performance of face super resolution (FSR), most of these methods cannot recover essential face attributes accurately, especially to super‐resolve a very low‐resolution (LR) face image (16×16 pixels) to its (8×) high‐resolution (HR) version. To address this issue, a novel alternating optimisation algorithm to estimate facial attributes and restore facial images in a single network is presented . Specifically, two convolutional neural modules (denoted as Restorer and Corrector) are constructed and these two modules are alternated repeatedly to form an end‐to‐end trainable network. The Restorer module reconstructs face images based on estimated facial attributes, while the Corrector module corrects the estimated attributes with the help of restored face image. Since Corrector can utilise information from previous estimated attributes and the FSR image, the estimated attributes are iteratively corrected and gradually approach the ground truth. Moreover, a new attribute transformation scheme is designed to introduce attribution information into Restorer, in which the facial attribute vectors as the control conditions can guide the face image restoration explicitly. By conducting extensive experiments on the well‐known CelebA dataset, it is demonstrated that the proposed method can provide superior FSR performance in both quantitative and qualitative measurements.

【 授权许可】

Unknown