An image super-resolution algorithm for different error levels per frame

Authors:
Hu He;L. P. Kondi
Affiliations:
Dept. of Electr. Eng., Univ., Buffalo, NY, USA;-
Venue:
IEEE Transactions on Image Processing
Year:
2006

Citing 0
Cited 12

A Lorentzian stochastic estimation for a robust iterative multiframe super-resolution reconstruction with Lorentzian-Tikhonov regularization

EURASIP Journal on Advances in Signal Processing
Determining the regularization parameters for super-resolution problems

Signal Processing
A soft MAP framework for blind super-resolution image reconstruction

Image and Vision Computing
Region-Based Super Resolution for Video Sequences Considering Registration Error

PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Example-based image super-resolution with class-specific predictors

Journal of Visual Communication and Image Representation
Hallucinating face by position-patch

Pattern Recognition
Stochastic super-resolution image reconstruction

Journal of Visual Communication and Image Representation
Performance of reconstruction-based super-resolution with regularization

Journal of Visual Communication and Image Representation
Adaptive multiple-frame image super-resolution based on U-curve

IEEE Transactions on Image Processing
Region-based weighted-norm with adaptive regularization for resolution enhancement

Digital Signal Processing
A robust multiframe super-resolution algorithm based on half-quadratic estimation with modified BTV regularization

Digital Signal Processing
Accurate image registration for MAP image super-resolution

Image Communication

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we propose an image super-resolution (resolution enhancement) algorithm that takes into account inaccurate estimates of the registration parameters and the point spread function. These inaccurate estimates, along with the additive Gaussian noise in the low-resolution (LR) image sequence, result in different noise level for each frame. In the proposed algorithm, the LR frames are adaptively weighted according to their reliability and the regularization parameter is simultaneously estimated. A translational motion model is assumed. The convergence property of the proposed algorithm is analyzed in detail. Our experimental results using both real and synthetic data show the effectiveness of the proposed algorithm.