23 research outputs found
Learning to Hallucinate Face Images via Component Generation and Enhancement
We propose a two-stage method for face hallucination. First, we generate
facial components of the input image using CNNs. These components represent the
basic facial structures. Second, we synthesize fine-grained facial structures
from high resolution training images. The details of these structures are
transferred into facial components for enhancement. Therefore, we generate
facial components to approximate ground truth global appearance in the first
stage and enhance them through recovering details in the second stage. The
experiments demonstrate that our method performs favorably against
state-of-the-art methodsComment: IJCAI 2017. Project page:
http://www.cs.cityu.edu.hk/~yibisong/ijcai17_sr/index.htm
Hallucinating very low-resolution and obscured face images
Most of the face hallucination methods are designed for complete inputs. They
will not work well if the inputs are very tiny or contaminated by large
occlusion. Inspired by this fact, we propose an obscured face hallucination
network(OFHNet). The OFHNet consists of four parts: an inpainting network, an
upsampling network, a discriminative network, and a fixed facial landmark
detection network. The inpainting network restores the low-resolution(LR)
obscured face images. The following upsampling network is to upsample the
output of inpainting network. In order to ensure the generated
high-resolution(HR) face images more photo-realistic, we utilize the
discriminative network and the facial landmark detection network to better the
result of upsampling network. In addition, we present a semantic structure
loss, which makes the generated HR face images more pleasing. Extensive
experiments show that our framework can restore the appealing HR face images
from 1/4 missing area LR face images with a challenging scaling factor of 8x.Comment: 20 pages, Submitted to Pattern Recognition Letter
Reconstruction Low- Resolution Image Face Using Restricted Boltzmann Machine
Low-resolution (LR) face images are one of the most challenging problems in face recognition (FR) systems. Due to the difficulty of finding the specific features of faces, the accuracy of face recognition is low. To solve this problem, some researchers are using an image reconstruction approach to improve the resolution of their images. In this research, we are trying to use the restricted Boltzmann machine (RBM) to solve the problem. Furthermore, a labelled face in the wild (lfw) database has been used to validate the proposed method. The results of the experiment show that the PSNR and SSIM of the image result are 34.05 dB and 96.8%, respectively