Restoration of Images Corrupted by Impulse Noise and Mixed Gaussian Impulse Noise Using Blind Inpainting

Yan, Ming

doi:10.1137/12087178x

Cited by 148 publications

(144 citation statements)

References 42 publications

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“…There, individual pixels were eliminated from the region-based segmentation terms to prevent skewing and misleading the segmentation. This method is related to similar approaches in occlusion detection in optical flow [1] and salt-and-pepper denoising [73]. Here, the goal is to isolate defects and artifacts from interfering with the regular modes.…”

Section: Artifact Indicator Functionmentioning

confidence: 99%

Two-Dimensional Compact Variational Mode Decomposition

et al. 2017

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Decomposing multidimensional signals, such as images, into spatially compact, potentially overlapping modes of essentially wavelike nature makes these components accessible for further downstream analysis. This decomposition enables space-frequency analysis, demodulation, estimation of local orientation, edge and corner detection, texture analysis, denoising, inpainting, or curvature estimation. Our model decomposes the input signal into modes with narrow Fourier bandwidth; to cope with sharp region boundaries, incompatible with narrow bandwidth, we introduce binary support functions that act as masks on the narrow-band mode for image recomposition. L 1 and TV terms promote sparsity and spatial compactness. Constraining the support functions to partitions of the signal domain, we effectively get an image segmentation model based on spectral homogeneity. By coupling several submodes together with a single support function, we are able to decompose an image into several crystal grains. Our efficient algorithm is based on variable splitting and alternate direction optimization; we employ Merriman-Bence-Osher-like threshold dynamics to handle efficiently the motion by mean curvature of the support function boundaries under the sparsity promoting terms. The versatility and effectiveness of our proposed model is demonstrated on a broad variety of example images from different modalities. These demonstrations include the decomposition of images into overlapping modes with smooth or sharp boundaries, segmentation of images of crystal grains, and inpainting of damaged image regions through artifact detection.

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Section: Artifact Indicator Functionmentioning

confidence: 99%

Two-Dimensional Compact Variational Mode Decomposition

et al. 2017

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“…Digital image processing embraces an extensive and diverse field of applications. Under the strict decorum of the digital image processing and the computer aided peripherals, the image transformation and development have made it into the physical domains to smoothen the procedural flows of the paradigm [3]- [4].…”

Section: Introductionmentioning

confidence: 99%

“…Enhancement algorithms are usually dependent and interactive. It may include noise filtrations, de-blurring of images degraded by environment or its sensor's limitations, and corrections of non-linearity because of sensors [4].…”

Section: Introductionmentioning

confidence: 99%

Development of an Improved Algorithm for Image Processing: A Proposed Algorithm for Optimal Reduction of Shadow from the Image

Al-Halabi¹

2017

ijacsa

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Abstract-Shadow detection is the most important aspect in the field of image processing. It has become essential to develop such algorithms that are capable of processing the images with the maximum efficiency. Therefore, the research has aimed to propose an algorithm that effectively processes the image on the basis of shadow reduction. An algorithm has been proposed, which was based on RGB (red, green, and blue) and HIS (hue, saturation and intensity) model. Steps for Shadow detection have been defined. Median filter and colour saturation have been widely used to process the outcomes. Algorithm has proved efficient for the detection of shadow from the images. It was found efficient when compared with two previously developed algorithms. 87% efficiency has been observed, implementing the proposed algorithm as compared to the algorithms implemented previously by other researchers. The study proved to make a supportive effort in the development of optimized algorithm. It has been suggested that the market requires such practices that can be used to improve the working conditions of the image processing paradigm.

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“…It has been successfully applied in image reconstruction corrupted by impulse noise [10]. We notice that the samples corrupted by malicious users are sparsely distributed in these incomplete measurements, and the values corrupted by malicious users are limited in a range.…”

Section: Introductionmentioning

confidence: 99%

Low-rank matrix completion based malicious user detection in cooperative spectrum sensing

Qin

Gao

Plumbley

et al. 2013

2013 IEEE Global Conference on Signal and Information Processing

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Abstract-In a cognitive radio (CR) system, cooperative spectrum sensing (CSS) is the key to improving sensing performance in deep fading channels. In CSS networks, signals received at the secondary users (SUs) are sent to a fusion center to make a final decision of the spectrum occupancy. In this process, the presence of malicious users sending false sensing samples can severely degrade the performance of the CSS network. In this paper, with the compressive sensing (CS) technique being implemented at each SU, we build a CSS network with double sparsity property. A new malicious user detection scheme is proposed by utilizing the adaptive outlier pursuit (AOP) based low-rank matrix completion in the CSS network. In the proposed scheme, the malicious users are removed in the process of signal recovery at the fusion center. The numerical analysis of the proposed scheme is carried out and compared with an existing malicious user detection algorithm.

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Restoration of Images Corrupted by Impulse Noise and Mixed Gaussian Impulse Noise Using Blind Inpainting

Cited by 148 publications

References 42 publications

Two-Dimensional Compact Variational Mode Decomposition

Two-Dimensional Compact Variational Mode Decomposition

Development of an Improved Algorithm for Image Processing: A Proposed Algorithm for Optimal Reduction of Shadow from the Image

Low-rank matrix completion based malicious user detection in cooperative spectrum sensing

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