Multi-frame compression: theory and design
Signal Processing - Special section on signal processing technologies for short burst wireless communications
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
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 single image scale-up using sparse-representations
Proceedings of the 7th international conference on Curves and Surfaces
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Hi-index | 0.00 |
In this paper single image superresolution problem using sparse data representation is described. Image super-resolution is ill - posed inverse problem. Several methods have been proposed in the literature starting from simple interpolation techniques to learning based approach and under various regularization frame work. Recently many researchers have shown interest to super-resolve the image using sparse image representation. We slightly modified the procedure described by a similar work proposed recently. The modification suggested in the proposed approach is the method of dictionary training, feature extraction from the trained data base images and regularization. We have used singular values as prior for regularizing the ill-posed nature of the single image superresolution problem. Method of Optimal Directions algorithm (MOD) has been used in the proposed algorithm for obtaining high resolution and low resolution dictionaries from training image patches. Using the two dictionaries the given low resolution input image is superresolved. The results of the proposed algorithm showed improvements in visual, PSNR, RMSE and SSIM metrics over other similar methods.