International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Noise Estimation from a Single Image
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image upsampling via imposed edge statistics
ACM SIGGRAPH 2007 papers
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Face Hallucination: Theory and Practice
International Journal of Computer Vision
ACM SIGGRAPH Asia 2008 papers
Visio-lization: generating novel facial images
ACM SIGGRAPH 2009 papers
Personal photo enhancement using example images
ACM Transactions on Graphics (TOG)
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
Generalized Face Super-Resolution
IEEE Transactions on Image Processing
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This paper presents a new approach to generate a high quality facial image from a low resolution facial image, based on a large set of facial images belongs to the same person but varies in pose and expression. The input images are taken by low-end cameras or cameras from a long distance. The facial poses and expressions are not consistent and aligned. Firstly, using a low resolution facial image as a query image, a set of high resolution images with similar pose and expression is retrieved from the image examples by the proposed similarity measurement based on its shape and texture information of the query image. The selected images are then aligned with the query image and used as the candidates for the face hallucination. A Markov random field (MRF) model based on a new proposed color and edge constraints is introduced to find an optimum solution for the hallucination image. In the experiments, high textural details of hallucination images which are four to eight times larger than the original low resolution images were generated by the proposed face hallucination approach. The high resolution outputs of our method are significantly improved in quality compared to other image superresolution methods. Moreover, we also showed that our new approach is able to handle underexposure and noisy images.