A multi-frame super-resolution method based on the variable-exponent nonlinear diffusion regularizer

Maiseli, Baraka; Elisha, Ogada Achieng; Gao, Huijun

doi:10.1186/s13640-015-0077-2

Cited by 21 publications

(7 citation statements)

References 53 publications

(50 reference statements)

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“…The p-Laplacian scale space, for p ∈ [1,2], has increasingly attracted attention in recent years [9,10,11,12,13,14,15,16,17]. In the studies [18,19,20] by Kuijper, this flow was analyzed in terms of gauge coordinates.…”

Section: Introductionmentioning

confidence: 99%

Introducing the p-Laplacian spectra

Cohen

Gilboa

2020

Signal Processing

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In this work we develop a nonlinear decomposition, associated with nonlinear eigenfunctions of the p-Laplacian for p ∈ (1, 2). With this decomposition we can process signals of different degrees of smoothness.We first analyze solutions of scale spaces, generated by γ-homogeneous operators, γ ∈ R. An analytic solution is formulated when the scale space is initialized with a nonlinear eigenfunction of the respective operator. We show that the flow is extinct in finite time for γ ∈ [0, 1).A main innovation in this study is concerned with operators of fractional homogeneity, which require the mathematical framework of fractional calculus.The proposed transform rigorously defines the notions of decomposition, reconstruction, filtering and spectrum. The theory is applied to the p-Laplacian operator, where the tools developed in this framework are demonstrated.

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Section: Introductionmentioning

confidence: 99%

Introducing the p-Laplacian spectra

Cohen

Gilboa

2020

Signal Processing

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“…e main aim of the regularization method is to resolve the SR issue due to its ill-conditional nature. Recent works have been conducted aiming at resolving the SR issue by exploiting the regularization framework [8][9][10][11][19][20][21][22][23][24][25][26]. e idea of reconstruction of the SR image relies on the regularization process that generates the HR image through attempting to minimize the main objective function, where it incorporates both the fidelity of data and the regularization terms.…”

Section: Introductionmentioning

confidence: 99%

Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

Khattab

Zeki

Alwan

et al. 2021

Journal of Electrical and Computer Engineering

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The primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approaches are typically affected by annoying restorative artifacts, including blurring, noise, and staircasing effect. Accordingly, it is always difficult to balance between smoothness and edge preservation. In this paper, we intend to enhance the efficiency of multiframe super-resolution image reconstruction in order to optimize both analysis and human interpretation processes by improving the pictorial information and enhancing the automatic machine perception. As a result, we propose new approaches that firstly rely on estimating the initial high-resolution image through preprocessing of the reference low-resolution image based on median, mean, Lucy-Richardson, and Wiener filters. This preprocessing stage is used to overcome the degradation present in the reference low-resolution image, which is a suitable kernel for producing the initial high-resolution image to be used in the reconstruction phase of the final image. Then, L2 norm is employed for the data-fidelity term to minimize the residual among the predicted high-resolution image and the observed low-resolution images. Finally, bilateral total variation prior model is utilized to restrict the minimization function to a stable state of the generated HR image. The experimental results of the synthetic data indicate that the proposed approaches have enhanced efficiency visually and quantitatively compared to other existing approaches.

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“…Super‐resolution refers to an image restoration problem that seeks to recover a high‐quality image from several degraded versions [1–8]. The process requires that pairs of input frames contain relative motions, usually rotations and translations, between them.…”

Section: Introductionmentioning

confidence: 99%

Diffusion‐steered super‐resolution method based on the Papoulis–Gerchberg algorithm

Maiseli

2016

IET Image Processing

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A multi-frame super-resolution method based on the variable-exponent nonlinear diffusion regularizer

Cited by 21 publications

References 53 publications

Introducing the p-Laplacian spectra

Introducing the p-Laplacian spectra

Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

Diffusion‐steered super‐resolution method based on the Papoulis–Gerchberg algorithm

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