&lt;title&gt;Preconditioned iterative methods for superresolution image reconstruction with multisensors&lt;/title&gt;

Ng, Michael K.; Sze, Kenton

doi:10.1117/12.406518

Cited by 5 publications

(10 citation statements)

References 3 publications

(5 reference statements)

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“…Experimental results by Ng and Sze (2000) show that the quality of reconstructed images is better than that of combined observed lowresolution images. However, the preconditioned conjugate gradient (PCG) method, with cosine transform-based preconditioners for solving corresponding linear systems arising from superresolution image reconstruction, is not very efficient (Ng and Sze, 2000). In this article, we propose a new model to give better reconstructed images than those in Eq.…”

Section: Introductionmentioning

confidence: 93%

“…Here, the coefficient matrix cannot be diagonalized by DCT matrix or approximated well by cosine transform preconditioners (Ng and Sze, 2000). Therefore, if we apply the preconditioned conjugate method to solve this system, the number of iterations required for convergence will be more; see the numerical results in Ng and Sze (2000).…”

Section: Lemma 2: the Hessian Hess( J) Of F( F U) Is Positive Definimentioning

confidence: 99%

“…Therefore, if we apply the preconditioned conjugate method to solve this system, the number of iterations required for convergence will be more; see the numerical results in Ng and Sze (2000). Moreover, the quality of reconstructed images with the minimization model in Eq.…”

Section: Lemma 2: the Hessian Hess( J) Of F( F U) Is Positive Definimentioning

confidence: 99%

“…The mathematical formulation that we used in the image reconstruction problem is stated as follows (Bose and Boo, 1998;Ng and Sze, 2000):…”

Section: Introductionmentioning

confidence: 99%

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Superresolution image reconstruction from blurred observations by multisensors

Ching

Sze

et al. 2003

Int J Imaging Syst Tech

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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.

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Section: Introductionmentioning

confidence: 93%

Section: Lemma 2: the Hessian Hess( J) Of F( F U) Is Positive Definimentioning

confidence: 99%

Section: Lemma 2: the Hessian Hess( J) Of F( F U) Is Positive Definimentioning

confidence: 99%

“…The mathematical formulation that we used in the image reconstruction problem is stated as follows (Bose and Boo, 1998;Ng and Sze, 2000):…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Superresolution image reconstruction from blurred observations by multisensors

Ching

Sze

et al. 2003

Int J Imaging Syst Tech

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“…Such regularization is called H 1 -norm regularization functional. Experimental results in Reference [12] show that the quality of reconstructed images is better than that of observed high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

Super‐resolution image reconstruction using multisensors

Ching

Sze

et al. 2004

Numerical Linear Algebra App

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SUMMARYSuper-resolution image reconstruction refers to obtaining an image at a resolution higher than that of a camera (sensor) used in recording the image. In this paper, we present a new joint minimization model in which an objective function is set up consisting of three terms: the data ÿtting term, the regularization terms for the reconstructed image and the observed low-resolution images. An alternating minimization iterative algorithm is proposed and developed to reconstruct the image. We give a convergence analysis of the alternating minimization iterative algorithm and show that it converges for H 1 -norm regularization functional. Numerical examples are given to illustrate the e ectiveness of the joint minimization model and the e ciency of the algorithm.

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Superresolution image reconstruction using fast inpainting algorithms

Chan

Yau

et al. 2007

Applied and Computational Harmonic Analysis

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The main aim of this paper is to employ the total variation (TV) inpainting model to superresolution imaging problems. We focus on the problem of reconstructing a high-resolution image from several decimated, blurred and noisy low-resolution versions of the high-resolution image. We propose a general framework for multiple shifted and multiple blurred low-resolution image frames which subsumes several well-known superresolution models. Moreover, our framework allows an arbitrary pattern of missing pixels and in particular missing frames. The proposed model combines the TV inpainting model with the framework to formulate the superresolution image reconstruction problem as an optimization problem. A distinct feature of our model is that in regions without missing pixels, the reconstruction process is regularized by TV minimization whereas in regions with missing pixels or missing frames, they are reconstructed automatically by means of TV inpainting. A fast algorithm based on fixed-point iterations and preconditioning techniques is investigated to solve the associated Euler-Lagrange equations. Experimental results are given to show that the proposed TV superresolution imaging model is effective and the proposed algorithm is efficient.

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<title>Preconditioned iterative methods for superresolution image reconstruction with multisensors</title>

Cited by 5 publications

References 3 publications

Superresolution image reconstruction from blurred observations by multisensors

Superresolution image reconstruction from blurred observations by multisensors

Super‐resolution image reconstruction using multisensors

Superresolution image reconstruction using fast inpainting algorithms

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