Rapid and efficient image restoration technique based on new adaptive anisotropic diffusion function

Tebini, Sondes; Mbarki, Zouhair; Seddik, Hassene; Braiek, Ezzedine Ben

doi:10.1016/j.dsp.2015.09.013

Cited by 38 publications

(9 citation statements)

References 25 publications

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“…The AnisDM is widely used in digital image processing [57,58] for tasks such as image denoising, restoration [57], and segmentation [58]. Unlike the traditional diffusion model [13,42,50] with constant diffusion coefficients, the AnisDM allows a variable diffusion rate per grid cell in a geographic area.…”

Section: Diffusion Coefficient Functionmentioning

confidence: 99%

Anisotropic Diffusion for Improved Crime Prediction in Urban China

Tang

Zhu

Guo

et al. 2019

IJGI

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As a major social issue during urban development, crime is closely related to socioeconomic, geographic, and environmental factors. Traditional crime prediction models reveal the spatiotemporal dynamics of crime risks, but usually ignore the environmental context of the geographic areas where crimes occur. Therefore, it is difficult to enhance the spatial accuracy of crime prediction. We propose the use of anisotropic diffusion to include environmental factors of the evaluated geographic area in the traditional crime prediction model, thereby aiming to predict crime occurrence at a finer scale regarding spatiotemporal aspects and environmental similarity. Under different evaluation criteria, the average prediction accuracy of the proposed method is 28.8%, improving prediction accuracy by 77.5%, as compared to the traditional methods. The proposed method can provide strong policing support in terms of conducting targeted hotspot policing and fostering sustainable community development.

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Section: Diffusion Coefficient Functionmentioning

confidence: 99%

Anisotropic Diffusion for Improved Crime Prediction in Urban China

Tang

Zhu

Guo

et al. 2019

IJGI

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“…Compressed sensing techniques are having its own benefits in the restoration and these requires further image processing technique to improve the image quality [5]. Linear smoothing techniques provide good results, but reduce the visual details of image, that causes the image not exploitable for further image restoration [6].…”

Section: Introductionmentioning

confidence: 99%

Robust Image Restoration using Non-Local Graph Based Transform and Wave Atoms

Ganesh¹,

Kusagur²

2019

IJIES

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The image restoration is the process of recovering the uncorrupted image from the noisy or blurred image. The aim of this method is to propose a hybrid method for effective image restoration process. The Hybrid involves in Non-Local Graph based Transform (NLGBT) learns basic vectors from the image and Wave atom technique is applied for harmonic computational analysis. In order to test the performance of the proposed system, inbuilt MATLAB image is considered and these images has been degraded with Gaussian blur, then added to the Gaussian noise. The degraded images are used to plot NLGBT and the basic vectors learn from this graph. Wave atoms coefficient has been set with the threshold values, which smoothen the image with provided vector. Instead of smoothening of the images, smoothening on the given basic vectors of the images from the NLGBT technique helps in achieving higher efficiency. The existing methods involves in smoothing of images for denoising and proposed hybrid method involves in smoothening of basic vectors from NLGBT. The result provided by the wave atom technique achieves the PSNR value of 18.57 dB, state-of-the-art method achieves 20.08 dB and proposed technique achieve up to 29.02. The experimental result shows that the NLGBT and wave atoms attains high quality of the image in the restoration process compared to other existing method.

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“…Xu et al [11] suggested an adaptive thresholding in PM diffusion coefficient to better handle the diffusion as time elapsed. A new diffusion coefficient has been proposed by Tebini et al [12,13] for better control of the diffusion process in regions containing edge. Wang et al [14] proposed new second-and fourth-order anisotropic equations for denoising.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising Based on Adaptive Fractional Order with Improved PM Model

Zhai

Zhou

et al. 2018

Mathematical Problems in Engineering

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In order to improve the image quality, in this paper, we propose an improved PM model. In the proposed model, we introduce two novel diffusion coefficients and a residual error term and replace the integer differential operator with the fractional differential operator in the PM model. The diffusion coefficients can be used effectively for edge detection and noise removal. The residual error term can help to prevent image distortion. Fractional order differential operator has a good characteristic that it can enhance image texture information while removing image noise. Additionally, in the two new diffusion coefficients, a novel method is proposed for automatically setting parameter k, and it does not need to do any experiments to get the value of k. For the computing fractional order diffusion coefficient, we employ the discrete Fourier transform, and an iterative scheme is carried out in the frequency domain. In the proposed model, not only is the integer differential operator replaced with the fractional differential operator, but also the order of the fractional differentiation is determined adaptively with the local variance. Comparing with some existing models, the experimental results show that the proposed algorithm can not only better suppress noise, but also better preserve edge and texture information. Moreover, the running time is greatly reduced.

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Rapid and efficient image restoration technique based on new adaptive anisotropic diffusion function

Cited by 38 publications

References 25 publications

Anisotropic Diffusion for Improved Crime Prediction in Urban China

Anisotropic Diffusion for Improved Crime Prediction in Urban China

Robust Image Restoration using Non-Local Graph Based Transform and Wave Atoms

Image Denoising Based on Adaptive Fractional Order with Improved PM Model

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