Image resolution enhancement using subpixel camera displacement

Jacquemod, Gilles; Odet, C.; Goutte, R.

doi:10.1016/0165-1684(92)90060-a

Cited by 29 publications

(16 citation statements)

References 1 publication

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“…The conjugate gradient method is employed to solve the preconditioned system (8). The stopping criterion is , where is the normal equations residual after iterations.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Thus, we conclude that the preconditioned conjugate gradient method applied to (8) with will converge linearly with . Since has only nonzero diagonals, the matrix-vector product can be done in operations.…”

Section: B Convergence Analysismentioning

confidence: 99%

“…In this section, we study the convergence rate of the preconditioned conjugate gradient method for solving the linear systems (8) where is a positive constant. We remark that when the MAP, norm or norm regularization functional is used, the corresponding regularization matrices under the Neumann boundary condition can be diagonalized by the discrete cosine transform matrix.…”

Section: B Convergence Analysismentioning

confidence: 99%

“…More general high-resolution (or super-resolution) image reconstruction problems can be found in [8], [18], [20].…”

Section: The Mathematical Modelmentioning

confidence: 99%

“…Multiple undersampled images of a scene are often obtained by using multiple identical image sensors which are shifted relative to each other by subpixel displacements [12], [8]. The resulting high-resolution image reconstruction problem using a set of currently-available image sensors is interesting because 1057-7122/02$17.00 © 2002 IEEE it is closely related to the design of high-definition television (HDTV) and very high-definition (VHD) image sensors.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of displacement errors in high-resolution image reconstruction with multisensors

Bose

2002

IEEE Trans. Circuits Syst. I

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Abstract-An image-acquisition system composed of an array of sensors, where each sensor has a subarray of sensing elements of suitable size, has recently been popular for increasing the spatial resolution with high signal-to-noise ratio beyond the performance bound of technologies that constrain the manufacture of imaging devices. Small perturbations around the ideal subpixel locations of the sensing elements (responsible for capturing the sequence of undersampled degraded frames), because of imperfections in fabrication, limit the performance of the signal-processing algorithms for processing and integrating the acquired images for the desired enhanced resolution and quality. The contributions of this paper include an analysis of the displacement errors on the convergence rate of the iterative approach for solving the transform based preconditioned system of equations. Subsequently, it is established that the use of the MAP, 2 norm or 1 norm regularization functional leads to a proof of linear convergence of the conjugate gradient method in terms of the displacement errors caused by the imperfect subpixel locations. Results of simulation support the analytical results.Index Terms-Displacement-error analysis, high resolutin, image reconstruction, multisensors.

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“…The conjugate gradient method is employed to solve the preconditioned system (8). The stopping criterion is , where is the normal equations residual after iterations.…”

Section: Numerical Resultsmentioning

confidence: 99%

Section: B Convergence Analysismentioning

confidence: 99%

Section: B Convergence Analysismentioning

confidence: 99%

“…More general high-resolution (or super-resolution) image reconstruction problems can be found in [8], [18], [20].…”

Section: The Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of displacement errors in high-resolution image reconstruction with multisensors

Bose

2002

IEEE Trans. Circuits Syst. I

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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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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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Image resolution enhancement using subpixel camera displacement

Cited by 29 publications

References 1 publication

Analysis of displacement errors in high-resolution image reconstruction with multisensors

Analysis of displacement errors in high-resolution image reconstruction with multisensors

Superresolution image reconstruction from blurred observations by multisensors

Super‐resolution image reconstruction using multisensors

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