Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Improved resolution from subpixel shifted pictures
CVGIP: Graphical Models and Image Processing
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
Wavelet Algorithms for High-Resolution Image Reconstruction
SIAM Journal on Scientific Computing
Efficient Super-Resolution and Applications to Mosaics
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Super-Resolution Reconstruction of Compressed Video Based on Adaptive Quantization Constraint Set
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
A super-resolution reconstruction algorithm for surveillance images
Signal Processing
Adaptive multiple-frame image super-resolution based on U-curve
IEEE Transactions on Image Processing
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Superresolution restoration of an image sequence: adaptive filtering approach
IEEE Transactions on Image Processing
Resolution enhancement of color video sequences
IEEE Transactions on Image Processing
Resolution enhancement of monochrome and color video using motion compensation
IEEE Transactions on Image Processing
Super-resolution reconstruction of hyperspectral images
IEEE Transactions on Image Processing
A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution
IEEE Transactions on Image Processing
Hi-index | 0.08 |
The spatial resolution of a hyperspectral image is often coarse because of the limitations of the imaging hardware. Super-resolution reconstruction (SRR) is a promising signal post-processing technique for hyperspectral image resolution enhancement. This paper proposes a maximum a posteriori (MAP) based multi-frame super-resolution algorithm for hyperspectral images. Principal component analysis (PCA) is utilized in both parts of the proposed algorithm: motion estimation and image reconstruction. A simultaneous motion estimation method with the first few principal components, which contain most of the information of a hyperspectral image, is proposed to reduce computational load and improve motion field accuracy. In the image reconstruction part, different image resolution enhancement techniques are applied to different groups of components, to reduce computational load and simultaneously remove noise. The proposed algorithm is tested on both synthetic images and real image sequences. The experimental results and comparative analyses verify the effectiveness of this algorithm.