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

Mourabit, Idriss El; Rhabi, Mohammed El; Hakim, Abdelilah; Laghrib, Amine; Moreau, Éric

doi:10.1016/j.sigpro.2016.09.014

Cited by 64 publications

(59 citation statements)

References 49 publications

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“…The classical software approach that has gained a considerable attention of scholars is called super-resolution [3][4][5][6], which uses signal processing principles to restore high-resolution images from at least one low-resolution image. Super-resolution techniques can be put into two major categories: single-frame-based, which generates a high-resolution image from the respective single low-resolution image [7,8], and multi-frame-based, which exploits information from a sequence of degraded images to generate a high-quality image [2,6].…”

Section: Colorimetry and Image Processingmentioning

confidence: 99%

Diffusion-Steered Super-Resolution Image Reconstruction

Maiseli¹

2018

Colorimetry and Image Processing

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For decades, super-resolution has been a widely applied technique to improve the spatial resolution of an image without hardware modification. Despite the advantages, super-resolution suffers from ill-posedness, a problem that makes the technique susceptible to multiple solutions. Therefore, scholars have proposed regularization approaches as attempts to address the challenge. The present work introduces a parameterized diffusion-steered regularization framework that integrates total variation (TV) and Perona-Malik (PM) smoothing functionals into the classical super-resolution model. The goal is to establish an automatic interplay between TV and PM regularizers such that only their critical useful properties are extracted to well pose the super-resolution problem, and hence, to generate reliable and appreciable results. Extensive analysis of the proposed resolution-enhancement model shows that it can respond well on different image regions. Experimental results provide further evidence that the proposed model outperforms.

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Section: Colorimetry and Image Processingmentioning

confidence: 99%

Diffusion-Steered Super-Resolution Image Reconstruction

Maiseli¹

2018

Colorimetry and Image Processing

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“…SR is a problem of obtaining a HR image from multiple or single LR images [10], which is an inverse problem of imaging process. In imaging process, the LR image is acquired through various imaging devices which are corrupted by noise and other degraded effect [11][12][13], and the imaging process is shown in Figure 1(a). It is worthwhile to improve the resolution of LR images in some special situations.…”

Section: Basic Framework Of Srmentioning

confidence: 99%

Superresolution Reconstruction of Electrical Equipment Incipient Fault

Wang

Guo

Chen

et al. 2018

Journal of Control Science and Engineering

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With the rapid development of industry and technology, the electrical power system becomes more complex and the electrical equipment becomes more diverse. Defective equipment is often the cause of industrial accidents and electrical injuries, which can result in serious injuries, such as electrocution, burns, and electrical shocks. In some cases, electrical equipment fault may result in death. However, in some special situation, some fault is very small even invisible, such as equipment aging, holes, and cracks, so the detection of these incipient faults is difficult or even impossible. These potential incipient faults become the biggest hidden danger in the electrical equipment and electricity power system. For these reasons, this paper proposes a superresolution reconstruction method for electrical equipment incipient fault to ensure complete detection in electrical equipment, which aims to guarantee the security of electrical power system operation and industry production. Experimental results show that this method can get a state-of-the-art reconstruction effect of incipient fault, so as to provide reliable fault detection of electrical power system.

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“…Sparse representation (SR) theory can describe and reconstruct images in a sparse and efficient way by linear combination of sparse coefficients and overcomplete dictionary [18]. In recent years, SR theory has been rapidly developed and successfully applied in many image processing applications, such as image super-resolution reconstruction [19,20], image feature extraction [21], image denoising [22,23], pedestrian re-identification [24,25], image classification [26], and many other fields. At the same time, it has been successfully applied to many image fusion fields and achieved some satisfactory results [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

A New Dictionary Construction Based Multimodal Medical Image Fusion Framework

Zhou

et al. 2019

Entropy

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Training a good dictionary is the key to a successful image fusion method of sparse representation based models. In this paper, we propose a novel dictionary learning scheme for medical image fusion. First, we reinforce the weak information of images by extracting and adding their multi-layer details to generate the informative patches. Meanwhile, we introduce a simple and effective multi-scale sampling to implement a multi-scale representation of patches while reducing the computational cost. Second, we design a neighborhood energy metric and a multi-scale spatial frequency metric for clustering the image patches with a similar brightness and detail information into each respective patch group. Then, we train the energy sub-dictionary and detail sub-dictionary, respectively by K-SVD. Finally, we combine the sub-dictionaries to construct a final, complete, compact and informative dictionary. As a main contribution, the proposed online dictionary learning can not only obtain an informative as well as compact dictionary, but can also address the defects, such as superfluous patch issues and low computation efficiency, in traditional dictionary learning algorithms. The experimental results show that our algorithm is superior to some state-of-the-art dictionary learning based techniques in both subjective visual effects and objective evaluation criteria.

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

Cited by 64 publications

References 49 publications

Diffusion-Steered Super-Resolution Image Reconstruction

Diffusion-Steered Super-Resolution Image Reconstruction

Superresolution Reconstruction of Electrical Equipment Incipient Fault

A New Dictionary Construction Based Multimodal Medical Image Fusion Framework

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