Resolution Enhancement by Prediction of the High-Frequency Image Based on the Laplacian Pyramid

Jeon, Bo-Won; Park, Rae-Hong; Yang, Seungjoon

doi:10.1155/asp/2006/72520

Cited by 21 publications

(12 citation statements)

References 12 publications

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“…Nevertheless MSRR, constructed on an iterative ML regularized technique, has the capability to increase the spatial resolution while eliminate the noise, the spatial enhance ratio rate of this MSRR is limited to 5.7x for theoretical case and to 1.6x for real case because of the algebraic limitation of this reconstruction MSRR [3,16]. The SSRR, constructed on high-frequency pre-forecasting [1], has the capability to increase the spatial resolution from a single captured image however the SSRR has less efficiency in the noisy case [13]. In order to bring the superiority of both MSRR for increasing the spatial resolution while eliminating the noise and SSRR for increasing the spatial resolution from a single captured image, this paper presents an extra-rate spatial enhancement constructed by MSRR [10] and SSRR [1,13] for spatial enhancing up to 16x ratio rate.…”

Section: The Research Problem and Mo-tivationmentioning

confidence: 99%

“…The SSRR, constructed on high-frequency pre-forecasting [1], has the capability to increase the spatial resolution from a single captured image however the SSRR has less efficiency in the noisy case [13]. In order to bring the superiority of both MSRR for increasing the spatial resolution while eliminating the noise and SSRR for increasing the spatial resolution from a single captured image, this paper presents an extra-rate spatial enhancement constructed by MSRR [10] and SSRR [1,13] for spatial enhancing up to 16x ratio rate.…”

Section: The Research Problem and Mo-tivationmentioning

confidence: 99%

“…This section introduces the algebraic theory of the extra-rate spatial enhancement, which is unified on MSRR [10] and SSRR [1] and the overall computed diagram of the spatial enhancement is illustrated in Fig. 1.…”

Section: The Algebraic Theory Of the Novel Resolution Enhancement Estmentioning

confidence: 99%

“…Initially, for restoring the fine image information while suppressing the noise, a group of captured LR images is processed by using MSRR using a regularized technique constructed on a recursive ML and Tukey's Biweight norm [10], which is comprehensively presented in section 2.1, to build the finer quality image with 4x ratio rate. Subsequently, the 4x enlarged image is processed to build the 16x enlarge image by using SSRR constructed on the high-frequency preforecasting [1], which is comprehensively presented in section 2.2.…”

Section: The Algebraic Theory Of the Novel Resolution Enhancement Estmentioning

confidence: 99%

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An Extra-Rate Spatial Enhancement Constructed by MSRR using Regularized Technique and SSRR using High-Frequency Pre-Forecasting

Patanavijit

Thakulsukanant

2018

ECTI-CIT

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Under many circumstances, a high spatial resolution image (HR) is greatly needed for modern applications nevertheless the HR image captured device is usually overpriced cost. Hence, Super Resolution Reconstruction (SRR) technique, which can reconstruct a HR image from a single LR image or many LR images by using algebraic formulation, is one of the modern research fields in digital image processing (DIP) and Computer Vision (CV). In this paper, an extra-rate spatial enhancement constructed by MSRR (Multi-frame Super Resolution Reconstruction) using regularized technique and SSRR (Single-frame Super Resolution Reconstruction) using high-frequency pre-forecasting is presented in order to enlarge up to 16x ratio rate. Initially, a group of captured images with low spatial resolution are mathematically fused by MSRR using regularized technique established on a recursive Maximum Likelihood and Tukey's Biweight norm in order to enlarge up to 4x ratio rate. Next, this 4x enhanced image is enlarged to be 16x spatial resolution image by SSRR established on the high-frequency pre-forecasting. In the verification experimentation section, the verification outcome demonstrates that the proposed spatial enhancement is successful for enlarging HR image with 16x ratio rate with finer quality.

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Section: The Research Problem and Mo-tivationmentioning

confidence: 99%

Section: The Research Problem and Mo-tivationmentioning

confidence: 99%

Section: The Algebraic Theory Of the Novel Resolution Enhancement Estmentioning

confidence: 99%

Section: The Algebraic Theory Of the Novel Resolution Enhancement Estmentioning

confidence: 99%

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An Extra-Rate Spatial Enhancement Constructed by MSRR using Regularized Technique and SSRR using High-Frequency Pre-Forecasting

Patanavijit

Thakulsukanant

2018

ECTI-CIT

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“…Laplacian pyramid [77] is also exploited to predict the high-resolution image based on the Gaussian/Laplacian pyramid structure.…”

Section: Iterative Approachmentioning

confidence: 99%

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

Tian¹

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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...

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Learning-Based Image Restoration for Compressed Image through Neighboring Embedding

Zhao

et al. 2008

Advances in Multimedia Information Processing - PCM 2008

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Resolution Enhancement by Prediction of the High-Frequency Image Based on the Laplacian Pyramid

Cited by 21 publications

References 12 publications

An Extra-Rate Spatial Enhancement Constructed by MSRR using Regularized Technique and SSRR using High-Frequency Pre-Forecasting

An Extra-Rate Spatial Enhancement Constructed by MSRR using Regularized Technique and SSRR using High-Frequency Pre-Forecasting

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

Learning-Based Image Restoration for Compressed Image through Neighboring Embedding

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