Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning

Autor: Liang Lin, Keze Wang, Guanbin Li, Qingxing Cao, Yukai Shi
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
Face hallucination
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
Iterative reconstruction
Facial recognition system
Machine Learning
Artificial Intelligence
Image Processing
Computer-Assisted
0202 electrical engineering
electronic engineering
information engineering
Humans
Reinforcement learning
Image resolution
Image restoration
business.industry
Applied Mathematics
Pattern recognition
Visualization
Recurrent neural network
Computational Theory and Mathematics
Face
Face (geometry)
020201 artificial intelligence & image processing
Neural Networks
Computer
Computer Vision and Pattern Recognition
Artificial intelligence
business
Algorithms
Software
DOI: 10.48550/arxiv.1905.01509
Popis: Face hallucination is a domain-specific super-resolution problem that aims to generate a high-resolution (HR) face image from a low-resolution~(LR) input. In contrast to the existing patch-wise super-resolution models that divide a face image into regular patches and independently apply LR to HR mapping to each patch, we implement deep reinforcement learning and develop a novel attention-aware face hallucination (Attention-FH) framework, which recurrently learns to attend a sequence of patches and performs facial part enhancement by fully exploiting the global interdependency of the image. Specifically, our proposed framework incorporates two components: a recurrent policy network for dynamically specifying a new attended region at each time step based on the status of the super-resolved image and the past attended region sequence, and a local enhancement network for selected patch hallucination and global state updating. The Attention-FH model jointly learns the recurrent policy network and local enhancement network through maximizing a long-term reward that reflects the hallucination result with respect to the whole HR image. Extensive experiments demonstrate that our Attention-FH significantly outperforms the state-of-the-art methods on in-the-wild face images with large pose and illumination variations.
Comment: To be published in TPAMI
Databáze: OpenAIRE
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