A Generalized Detail-Preserving Super-Resolution method

Zhao, Shengrong; Liang, Hu; Sarem, Mudar

doi:10.1016/j.sigpro.2015.09.006

Cited by 24 publications

(16 citation statements)

References 34 publications

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“…[39] (d) Adaptive reg. [67] (e) GDP SR [73] (f) The PDE in [15] (g) The proposed PDE (h) The HR image (c) TV reg. [39] (d) Adaptive reg.…”

Section: Discussionmentioning

confidence: 99%

“…[17] (c) TV reg. [39] (d) GDP SR [73] (e) The PDE in [15] (f) The proposed PDE Figure 12. Comparisons of different SR methods (Paint image).…”

Section: Discussionmentioning

confidence: 99%

“…The Non-Local Total Variation (NLTV) [11] is also proposed in the SR context [46] but suffers from the creation of artificial edges. Recently, motivated by the advantages of the BTV model and NLTV model, an adaptive non-locale edge-preserving image prior model was proposed in [73] which preserve edges and also avoid blur creation. A more recent nonlocal Laplace regularization was proposed and have shown very promising results [30].…”

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confidence: 99%

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A nonlinear fourth-order PDE for multi-frame image super-resolution enhancement

Laghrib¹,

Chakib²,

Hadri³

et al. 2020

Discrete &Amp; Continuous Dynamical Systems - B

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The multiframe super-resolution (SR) techniques are considered as one of the active research fields. More precisely, the construction of the desired high resolution (HR) image with less artifacts in the SR models, which are always ill-posed problems, requires a great care. In this paper, we propose a new fourth-order equation based on a diffusive tensor that takes the benefit from the diffusion model of Perona-Malik in the flat regions and the Weickert model near boundaries with a high diffusion order. As a result, the proposed SR approach can efficiently preserve image features such as corner and texture much better with less blur near edges. The existence and uniqueness of the proposed partial differential equation (PDE) are also demonstrated in an appropriate functional space. Finally, the given experimental results show the effectiveness of the proposed PDE compared to some competitive methods in both visually and quantitatively.

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“…[39] (d) Adaptive reg. [67] (e) GDP SR [73] (f) The PDE in [15] (g) The proposed PDE (h) The HR image (c) TV reg. [39] (d) Adaptive reg.…”

Section: Discussionmentioning

confidence: 99%

“…[17] (c) TV reg. [39] (d) GDP SR [73] (e) The PDE in [15] (f) The proposed PDE Figure 12. Comparisons of different SR methods (Paint image).…”

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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A nonlinear fourth-order PDE for multi-frame image super-resolution enhancement

Laghrib¹,

Chakib²,

Hadri³

et al. 2020

Discrete &Amp; Continuous Dynamical Systems - B

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“…There is a great number of proposed regularisations in the multi-frame superresolution context [36,35,34,26]. The main principle of these approaches is to provide a global minimum to the encountered minimization problem.…”

Section: Muti-frame Sr Problem Formulationmentioning

confidence: 99%

“…After the first work proposed in [19], where the authors considered a frequency domain approach, several approaches have been proposed and studied to improve the multi-frame SR problem [20,21,22,23,24]. Earlier works on SR algorithms are based on regularization method due to its ill-posed nature which mainly contains the likelihood and prior function [25,26]. The likelihood function measures the difference between the LR images and the obtained HR one, while the image prior function, impose some prior knowledge on the desired HR image.…”

Section: Introductionmentioning

confidence: 99%

A new denoising model for multi-frame super-resolution image reconstruction

et al. 2017

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Multi-frame image super-resolution (SR) aims to combine the sub-pixel information from a sequence of low-resolution (LR) images to build a highresolution (HR) one. SR techniques usually suffers from annoying restoration artifacts such as noise, jagged edges, and staircasing effect. In this paper, we aim to increase the performance of SR reconstitution under a variational framework using adaptive diffusion-based regularization term. We propose a new tensor based diffusion regularization that takes the benefit from the diffusion model of Perona-Malik in the flat regions and use a nonlinear tensor derived from the diffusion process of weickert filter near boundaries. Thus, the proposed SR approach can preserve important image features (sharp edges and corners) much better while avoiding artifacts. The synthetic and real experimental results show the effectiveness of the proposed regularisation compared to other methods in both quantitatively and visually.

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