2003
DOI: 10.1002/ima.10053
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Superresolution image reconstruction from blurred observations by multisensors

Abstract: 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 minimiz… Show more

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Cited by 4 publications
(2 citation statements)
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References 41 publications
(33 reference statements)
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“…Recently, Chan et al (2003) looked at the high-resolution image reconstruction problem from the wavelet point of view. Bose et al (1994), Ching et al (2003), and Ng et al (2002) considered the registration errors in the system matrix H (l1,l2) (␦) and proposed the total least squares method to minimize such errors.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, Chan et al (2003) looked at the high-resolution image reconstruction problem from the wavelet point of view. Bose et al (1994), Ching et al (2003), and Ng et al (2002) considered the registration errors in the system matrix H (l1,l2) (␦) and proposed the total least squares method to minimize such errors.…”
Section: Introductionmentioning
confidence: 99%
“…The above-mentioned multiple low-resolution images can either have relative geometric transformations among each other, or have different blurs [28][29][30][31] or zoomed factors [32][33][34][35] that are incurred during image acquisition. Furthermore, which is shifted relative to each other by user-controlled values [36][37][38][39][40][41].…”
Section: Discussionmentioning
confidence: 99%