High-resolution image reconstruction with multisensors

High-resolution image reconstruction arises in many applications, such as remote sensing, surveillance, and medical imaging. The model of Bose and Boo [2] can be viewed as the passage of the high-resolution image through a blurring kernel built from the tensor product of a univariate low-pass filter of the form 1 2 + , 1, · · · , 1, 1 2 − , where is the displacement error. When the number L of low-resolution sensors is even, tight frame symmetric framelet filters were constructed in [8] from this low-pass filter using the unitary extension principle of [43]. The framelet filters do not depend on , and hence the resulting algorithm reduces to that of the case where = 0. Furthermore, the framelet method works for symmetric boundary conditions. This greatly simplifies the algorithm. However, both the design of the tight framelets and extension to symmetric boundary are only for even L and cannot be applied to the case when L is odd. In this paper, we design tight framelets and derive a tight framelet algorithm with symmetric boundary conditions that work for both odd and even L. An analysis of the convergence of the algorithms is also given. The details of the implementations of the algorithm are also given.

Section: Mathematical Model For High-resolution Image Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The outline of the paper is as follows. In §2, we introduce the model by Bose and Boo [2]. In §3, we construct tight framelet systems for HR image reconstruction.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

High‐resolution image reconstruction with displacement errors: A framelet approach

Chan¹,

Riemenschneider²,

Shen³

et al. 2004

Constrained total least‐squares computations for high‐resolution image reconstruction with multisensors

Koo

Bose

2002

Self Cite

ABSTRACT:Multiple undersampled images of a scene are often obtained by using a charge-coupled device (CCD) detector array of sensors that are shifted relative to each other by subpixel displacements. This geometry of sensors, where each sensor has a subarray of sensing elements of suitable size, has been popular in the task of attaining spatial resolution enhancement from the acquired low-resolution degraded images that comprise the set of observations. With the objective of improving the performance of the signal processing algorithms in the presence of the ubiquitous perturbation errors of displacements around the ideal subpixel locations (because of imperfections in fabrication), in addition to noisy observation, the errors-invariables or the total least-squares method is used in this paper. A regularized constrained total least-squares (RCTLS) solution to the problem is given, which requires the minimization of a nonconvex and nonlinear cost functional. Simulations indicate that the choice of the regularization parameter influences significantly the quality of the solution. The L-curve method is used to select the theoretically optimum value of the regularization parameter instead of the unsound but expedient trial-and-error approach. The expected superiority of this RCTLS approach over the conventional least-squares theory-based algorithm is substantiated by example.

Superresolution image reconstruction from blurred observations by multisensors

Ching

Sze

et al. 2003

Superresolution image reconstruction refers to obtaining an image at a resolution higher than that of the camera (sensor) used in recording the image. In this article, we present a joint minimization model with an objective function setup that comprises three terms: the data-fitting term (DFT), the regularization term for the reconstructed image, and the observed low-resolution images. An alternating minimization iterative algorithm is presented to reconstruct the image. We also analyze the alternating minimization iterative algorithm and show that it converges globally for H 1 -norm or totalvariation regularization that are functional for the reconstructed image. Numeric examples are given to illustrate the effectiveness of the joint minimization model and the efficiency of the algorithm.