Improving image resolution using subpixel motion
Pattern Recognition Letters
Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Fast Iterative Super-Resolution for Image Sequences
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
Determining the regularization parameters for super-resolution problems
Signal Processing
Iterative-Interpolation Super-Resolution Image Reconstruction: A Computationally Efficient Technique
Iterative-Interpolation Super-Resolution Image Reconstruction: A Computationally Efficient Technique
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
On using high-definition body worn cameras for face recognition from a distance
BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
On single image scale-up using sparse-representations
Proceedings of the 7th international conference on Curves and Surfaces
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Improved face recognition at a distance using light field camera & super resolution schemes
Proceedings of the 6th International Conference on Security of Information and Networks
| Hi-index | 0.00 |
The characteristics of surveillance video generally include low-resolution images and blurred images. Decreases in image resolution lead to loss of high frequency facial components, which is expected to adversely affect recognition rates. Super resolution (SR) is a technique used to generate a higher resolution image from a given low-resolution, degraded image. Dictionary based super resolution pre-processing techniques have been developed to overcome the problem of low-resolution images in face recognition. However, super resolution reconstruction process, being ill-posed, and results in visual artifacts that can be visually distracting to humans and/or affect machine feature extraction and face recognition algorithms. In this paper, we investigate the impact of two existing super-resolution methods to reconstruct a high resolution from single/ multiple low-resolution images on face recognition. We propose an alternative scheme that is based on dictionaries in high frequency wavelet subbands. The performance of the proposed method will be evaluated on databases of high and low-resolution images captured under different illumination conditions and at different distances. We shall demonstrate that the proposed approach at level 3 DWT decomposition has superior performance in comparison to the other super resolution methods.