Image Super-Resolution Based on Alternative Registration, Blur Identification and Reconstruction

@inproceedings{Omer2011ImageSB,
  title={Image Super-Resolution Based on Alternative Registration, Blur Identification and Reconstruction},
  author={Osama Ahmed Omer},
  booktitle={SPIT/IPC},
  year={2011}
}
  • O. Omer
  • Published in SPIT/IPC 1 December 2011
  • Mathematics
A solution to the problem of obtaining a high-resolution image from several low-resolution images is provided. In general, this problem can be broken up into three sub-problems: registration, blur identification, and reconstruction. Conventional super-resolution approaches solve these sub-problems independently. In this paper, we propose a method to simultaneously solve all the sub-problems. The proposed method minimizes a nonlinear least squares error function. The cost function is… 

References

SHOWING 1-8 OF 8 REFERENCES
Blur Identification and Image Super-Resolution Reconstruction Using an Approach Similar to Variable Projection
TLDR
This letter proposes an approach for solving the joint blur identification and image SRR based on the principle similar to the variable projection method and proposes an efficient implementation based on Lanczos algorithm and Gauss quadrature theory.
Reconstruction of a high-resolution image by simultaneous registration, restoration, and interpolation of low-resolution images
TLDR
The low resolution to high resolution problem as a maximum likelihood (ML) problem which is solved by the expectation-maximization (EM) algorithm by exploiting the structure of the matrices involved, the problem ran be solved in the discrete frequency domain.
An image super-resolution algorithm for different error levels per frame
  • Hu He, L. Kondi
  • Computer Science
    IEEE Transactions on Image Processing
  • 2006
TLDR
An image super-resolution (resolution enhancement) algorithm that takes into account inaccurate estimates of the registration parameters and the point spread function and shows the effectiveness of the proposed algorithm.
Fast and robust multiframe super resolution
TLDR
This paper proposes an alternate approach using L/sub 1/ norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models and demonstrates its superiority to other super-resolution methods.
SELF ADAPTIVE BLIND SUPER RESOLUTION IMAGE RECONSTRUCTION
Super-Resolution (SR) image reconstruction is used to improve the clarity of the low resolution image. In our approach first image is scaled by gamma contrast. It relates to the pixel intensities of
General choice of the regularization functional in regularized image restoration
TLDR
A new paradigm is adopted, according to which the required prior information is extracted from the available data at the previous iteration step, i.e., the partially restored image at each step, allowing for the simultaneous determination of its value and the restoration of the degraded image.
Region-based weighted-norm with adaptive regularization for resolution enhancement
Lucas-Kanade 20 Years On: A Unifying Framework
TLDR
An overview of image alignment is presented, describing most of the algorithms and their extensions in a consistent framework and concentrating on the inverse compositional algorithm, an efficient algorithm that was recently proposed.