A Fast MAP Algorithm for High-Resolution Image Reconstruction with Multisensors

Authors:
Michael K. Ng;Andy M. Yip
Affiliations:
Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong. E-mail: mng@maths.hku.hk;Department of Mathematics, The University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095-1555
Venue:
Multidimensional Systems and Signal Processing
Year:
2001

Citing 9
Cited 8

Image resolution enhancement using subpixel camera displacement

Signal Processing
Fast Transform Based Preconditioners for ToeplitzEquations

SIAM Journal on Matrix Analysis and Applications
Fast recursive least squares adaptive filtering by fast Fourier transform-based conjugate gradient iterations

SIAM Journal on Scientific Computing
Conjugate Gradient Methods for Toeplitz Systems

SIAM Review
Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
A Fast Algorithm for Deblurring Models with Neumann Boundary Conditions

SIAM Journal on Scientific Computing
Digital Image Processing

Digital Image Processing
Digital Image Restoration

Digital Image Restoration
Extraction of high-resolution frames from video sequences

IEEE Transactions on Image Processing

Super-Resolution Image Restoration from Blurred Low-Resolution Images

Journal of Mathematical Imaging and Vision
Simultaneous estimation of super-resolved depth map and intensity field using photometric cue

Computer Vision and Image Understanding
Super-resolution reconstruction in a computational compound-eye imaging system

Multidimensional Systems and Signal Processing
A Bayesian super-resolution approach to demosaicing of blurred images

EURASIP Journal on Applied Signal Processing
Subpixel registration directly from the phase difference

EURASIP Journal on Applied Signal Processing
Approximation BFGS methods for nonlinear image restoration

Journal of Computational and Applied Mathematics
Fast minimization methods for solving constrained total-variation superresolution image reconstruction

Multidimensional Systems and Signal Processing
Fast algorithms for l1 norm/mixed l1 and l2 norms for image restoration

ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV

Quantified Score

Hi-index	0.00

Visualization

Abstract

In many applications, it is required to reconstruct a high-resolution image from multiple, undersampled and shifted noisy images. Using the regularization techniques such as the classical Tikhonov regularization and maximum a posteriori (MAP) procedure, a high-resolution image reconstruction algorithm is developed. Because of the blurring process, the boundary values of the low-resolution image are not completely determined by the original image inside the scene. This paper addresses how to use (i) the Neumann boundary condition on the image, i.e., we assume that the scene immediately outside is a reflection of the original scene at the boundary, and (ii) the preconditioned conjugate gradient method with cosine transform preconditioners to solve linear systems arising from the high-resolution image reconstruction with multisensors. The usefulness of the algorithm is demonstrated through simulated examples.