Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Improved resolution from subpixel shifted pictures
CVGIP: Graphical Models and Image Processing
The use of the L-curve in the regularization of discrete ill-posed problems
SIAM Journal on Scientific Computing
Wavelet Algorithms for High-Resolution Image Reconstruction
SIAM Journal on Scientific Computing
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
A frequency domain approach to registration of aliased images with application to super-resolution
EURASIP Journal on Applied Signal Processing
Determining the regularization parameters for super-resolution problems
Signal Processing
Robust Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Super-Resolution Reconstruction Algorithm To MODIS Remote Sensing Images
The Computer Journal
Super-Resolution of Multispectral Images
The Computer Journal
A super-resolution reconstruction algorithm for surveillance images
Signal Processing
Regularization Parameter Selection in Discrete Ill-Posed Problems-The Use of the U-Curve
International Journal of Applied Mathematics and Computer Science
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
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A computationally efficient superresolution image reconstruction algorithm
IEEE Transactions on Image Processing
Parameter estimation in Bayesian high-resolution image reconstruction with multisensors
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Super-resolution reconstruction of hyperspectral images
IEEE Transactions on Image Processing
Multiframe demosaicing and super-resolution of color images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
An image super-resolution algorithm for different error levels per frame
IEEE Transactions on Image Processing
A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution
IEEE Transactions on Image Processing
General choice of the regularization functional in regularized image restoration
IEEE Transactions on Image Processing
A super-resolution reconstruction algorithm for hyperspectral images
Signal Processing
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Adaptive regularization-based space-time super-resolution reconstruction
Image Communication
Hi-index | 0.01 |
Image super-resolution (SR) reconstruction has been a hot research topic in recent years. This technique allows the recovery of a high-resolution (HR) image from several low-resolution (LR) images that are noisy, blurred and down-sampled. Among the available reconstruction frameworks, the maximum a posteriori (MAP) model is widely used. In this model, the regularization parameter plays an important role. If the parameter is too small, the noise will not be effectively restrained; conversely, the reconstruction result will become blurry. Therefore, how to adaptively select the optimal regularization parameter has been widely discussed. In this paper, we propose an adaptive MAP reconstruction method based upon a U-curve. To determine the regularization parameter, a U-curve function is first constructed using the data fidelity term and prior term, and then the left maximum curvature point of the curve is regarded as the optimal parameter. The proposed algorithm is tested on both simulated and actual data. Experimental results show the effectiveness and robustness of this method, both in its visual effects and in quantitative terms.