Face hallucination using convolutional neural networks

摘要

Face hallucination using a bi-channel deep convolutional neural network (BCNN), which can adaptively fuse two channels of information. In one example, the BCNN is implemented to extract high level features from an input image. The extracted high level features are combined with low level details in the input image to produce the higher resolution image. Preferably, a proper coefficient is obtained to adaptively combine the high level features and the low level details.

主权项

1. A system for generating higher resolution output face images from input face images, the system comprising:
a convolutional neural network (CNN) that generates a face representation of an input face image, the CNN including convolution, non-linearity and down-sampling; a face hallucinator that generates a hallucinated face image from the face representation, the hallucinated face image having a higher resolution than the input face image; a coefficient estimator that generates a coefficient from the face representation; and a face combiner that combines the hallucinated face image with an up-sampled version of the input face image to produce an output face image, wherein the face combiner generates the output face image as a linear combination of the hallucinated face image and the up-sampled version of the input face image, and the coefficient determines the linear combination, wherein the output face image has more texture than the hallucinated face image.