Superresolution image reconstruction from blurred observations by multisensors

Ching, Wai‐Ki; Ng, Michael K.; Sze, Kenton; Yau, Andy C.

doi:10.1002/ima.10053

Cited by 4 publications

(2 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

High‐resolution image reconstruction from rotated and translated low‐resolution images with multisensors

Wen

Ching

2004

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

ABSTRACT:We extend the multisensor work by Bose and Boo (1998) and consider the perturbations of displacement error that are due to both translation and rotation. The warping process is introduced to obtain the ideal low-resolution image, which is located at exactly horizontal and vertical shift. In this approach, the problem of high-resolution image reconstruction is turned into the problem of image restoration, and the system becomes spatially invariant rather than spatially variant in the original problem. An efficient algorithm is presented. Experimental results show that the proposed methods are quite effective, and they perform better than the bilinear image interpolation method.

show abstract

Section: Introductionmentioning

confidence: 99%

High‐resolution image reconstruction from rotated and translated low‐resolution images with multisensors

Wen

Ching

2004

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

Super-resolution imaging : Markov chain Monte Carlo and state-space approaches

Tian¹

View full text Add to dashboard Cite

List of Figures 1.1 A conceptual framework of super-resolution imaging [1-19] 2 1.2 Various versions of the Text image: (a) original image (50 x 50); (b) enlarged image (200 x 200) by applying pixel duplication on the image (a); (c) enlarged image (200 x 200) by applying super-resolution algorithm on the image (a) 3 1.3 An overview of the proposed stochastic SR image approach using the Markov chain Monte Carlo techqnue 4 1.4 The proposed three-equation-based state-space representation for representing the relationships among the low-resolution frames and highresolution frames 6 2.1 The relationship between the original high-resolution image and the observed low-resolution images. The observed low-resolution images are the warped, blurred, down-sampled and noisy version of the original high-resolution image 11 2.2 The registration-interpolation-restoration-based approach for superresolution image reconstruction 17 2.3 A conceptual framework of learning-based SR image approach. ... 22 2.4 The sliding-window-based super-resolution video scheme 27 2.5 The simultaneous multiple high-resolution frames reconstruction scheme. 28 vii ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library LIST OF FIG URES LIST OF FIG URES 3.1 Various reconstructed results of Boat image: (a) the original image; (b) the simulated quarter-sized low-resolution image; (c) the bi-cubic spline interpolation approach, followed by deblurring (PSNR = 25.80 dB); (e) Vandewalle et ai's algorithm [20] (PSNR = 25.97 dB); (g) Pham et ai's algorithm [21] (PSNR = 26.61 dB); (i) Farsiu et a/.'s algorithm [22] (PSNR = 27.45 dB); (k) the proposed algorithm with the automatically-updated hyperparameter (PSNR = 28.02 dB); images (d), (f), (h), (j) and (1) are the absolute difference between image (a) and images (c), (e), (g), (i) and (k), respectively. Furthermore, the intensity values of these absolute difference images are scaled up three times for demonstration 49 3.2 Various reconstructed results of Text image: (a) the original image; (b) the simulated quarter-sized low-resolution image; (c) the bi-cubic spline interpolation approach, followed by deblurring (PSNR = 17.62 dB); (e) Vandewalle et a/.'s algorithm [20] (PSNR = 18.32 dB); (g) Pham et a/.'s algorithm [21] (PSNR = 17.74 dB); (i) Farsiu et aVs algorithm [22] (PSNR = 19.29 dB); (k) the proposed algorithm with the automatically-updated hyperparameter (PSNR = 20.17 dB); images (d), (f), (h), (j) and (1) are the absolute difference between image (a) and images (c), (e), (g), (i) and (k), respectively 50 viii ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library 65 3.10 The image sample, which is generated at each sample generation step of the proposed MCMC SR approach, is enhanced by adding a positive scaling factor j3 that is multiplied with a linear high-pass filtered version of the image sample 71 3.11 Three test images used in the experiments: (a) 256 x 256 Airplane; (b) 256 x...

show abstract