What's wrong with mean-squared error?
Digital images and human vision
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Real-time texture synthesis by patch-based sampling
ACM Transactions on Graphics (TOG)
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Hallucination: Theory and Practice
International Journal of Computer Vision
Face hallucination and recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Hallucinating face by eigentransformation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Quadratic interpolation for image resampling
IEEE Transactions on Image Processing
Image enhancement by nonlinear extrapolation in frequency space
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
In surveillance applications, cameras are usually set up with wide fields of view to capture as much of the scene as possible. This normally results in low-resolution images of the objects of interest. Since most image analysis applications require high or medium resolution inputs, the development of approaches aiming at improving the quality of these image regions has been an active research area in the last few years. A new family of approaches, based on statistical machine learning, aims at analyzing large data sets of images of a particular class of objects and learning the mapping from low-quality to high-quality images of that class. This enables them to infer, for example, the most likely high-resolution face image depicting the same person as a low-resolution image given as input. These super-resolution algorithms are time-consuming, due to the need for exhaustive search in a database of models. This work improves the efficiency of face image super-resolution using stochastic search for local modeling. Experimental results show that the proposed algorithm generates high-quality face images from low-resolution inputs while reducing the computation time dramatically.