The use of the L-curve in the regularization of discrete ill-posed problems
SIAM Journal on Scientific Computing
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Hallucination: Theory and Practice
International Journal of Computer Vision
Hallucinating face by position-patch
Pattern Recognition
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
On single image scale-up using sparse-representations
Proceedings of the 7th international conference on Curves and Surfaces
A Bayesian approach to image expansion for improved definition
IEEE Transactions on Image Processing
Geometry constrained sparse coding for single image super-resolution
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Multi-scale dictionary for single image super-resolution
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Because of the excellent ability to characterize the sparsity of natural images, @?"1-norm sparse representation (SR) is widely used to formulate the linear combination relationship in dictionary-learning-based face hallucination. However, due to inherently less sparse nature of noisy images, Laplacian prior assumed for @?"1-norm seems aggressive in terms of sparsity, which ultimately leads to significant degradation of hallucination performance in the presence of noise. To this end, we suggest a moderately sparse prior model referred to as a Gaussian-Laplacian mixture (GLM) distribution and employ it to infer the optimal solution under the Bayesian framework. The resulting regularization method known elastic net (EN) not only maintains same hallucination performance as SR under noise free scenarios but also outperforms the latter remarkably in the presence of noise. The experimental results on simulation and real-world noisy images show its superiority over some state-of-the-art methods.