Non-local means theory based Perona–Malik model for image denosing

Yang, Min; Liang, Jingkun; Zhang, Jianhai; Gao, Haidong; Meng, Fanqi; Li, Xingdong; Song, Sung‐Jin

doi:10.1016/j.neucom.2012.08.063

Cited by 39 publications

(32 citation statements)

References 22 publications

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“…In this section we will show that these effects will be reduced effectively by using the proposed diffusion coefficient. PM equation is widely used in the image denoising applications [32][33][34]. We implement our method similar to PM.…”

Section: Resultsmentioning

confidence: 99%

Efficient diffusion coefficient for image denoising

Rafsanjani

Sedaaghi

Saryazdi

2016

Computers & Mathematics with Applications

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a b s t r a c tDiffusion coefficient has an important role in the performance of partial differential equation (PDE) based image denoising techniques. Commonly, the classical Perona-Malik (PM) diffusion coefficient is widely used in PDE-based noise removal algorithms. In this paper, PM diffusion coefficient is analyzed regarding to its flux. Based on the analysis, PM flux for regions where the gradient magnitude is higher than smoothing threshold may lead to undesirable blurring effect and edge displacement. To address these issues, the image is divided into three segments based on the gradient magnitude: regions where the gradient is lower than the smoothing threshold, regions where the gradient is between the smoothing threshold and inflection point of flux, and regions where the gradient magnitude is higher than inflection point. We define the conditions that should be considered in these three segments. Then, a diffusion coefficient, satisfying all these conditions, is computed. Experimental results confirm the performance of the proposed method with regard to peak signal-to-noise ratio (PSNR), mean structural similarity (MSSIM), universal quality index (UQI), visual information fidelity (VIF), feature similarity (FSIM), information content weighted SSIM (IW-SSIM) and visual quality.

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Section: Resultsmentioning

confidence: 99%

Efficient diffusion coefficient for image denoising

Rafsanjani

Sedaaghi

Saryazdi

2016

Computers & Mathematics with Applications

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“…Basically, in image processing field, spatial frequency is a commonly used parameter describing the detailed information of a given image, such as texture and edge features. [24][25][26][27][28] Mathematically, definition of spatial frequency (SF) is given as…”

Section: Resultsmentioning

confidence: 99%

Automatic measurement of rotation center for laminography scanning system without dedicated phantoms

Yang

Liang

et al. 2014

J. Electron. Imaging

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With a particular function in plate-type structure tomography, computed laminography (CL) has received increasing attention in industrial nondestructive testing and become an important branch of computed tomography (CT). For the reconstruction algorithm of CL, center of rotation (COR) is the most important parameter determining the reconstruction accuracy and must be located precisely. Otherwise, even a tiny error of COR will cause obvious artifacts in reconstructed images. In order to realize measurement of COR with high accuracy and efficiency, a feasible calibration method was proposed to determine the position of COR without dedicated phantoms. According to this method, when a sample fixed on the rotational stage turns around the rotational axis, the locus of the sample's projection on the imaging plane will be an ellipse. Consistent with the symmetrical property of an elliptical image, a cross-correlation operation is adopted to determine the position of COR by locating the peak value of the cross-correlation function. The computer simulation and experimental results demonstrate that this method has high accuracy, and strong anti-noise and anti-wobble ability. In particular, this method does not need a dedicated phantom to perform the calibration, but rather uses projections of the inspected sample to calculate COR directly.

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“…Generally speaking, some “isolated noise points” (Zhang et al, ) similar to impulse noise always exist in low‐dose CT projection data. The useful image information will be focused only if the impulse noise in the original image can be effectively filtered (Yang et al, ). Note that the median filter is a useful tool that is powerful in filtering this type of pulse’ “isolated noise points.” Taking the relationship between the neighborhood median prior and the median filter into account, it is reasonable and useful for us to deal with the impulse noise in the original image by using the neighborhood median prior.…”

Section: Methodsmentioning

confidence: 99%

Sparse‐view statistical iterative head CT image reconstruction via joint regularization

Shangguan

Liu

Cui

et al. 2016

Int J Imaging Syst Tech

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It is a significant challenge to accurately reconstruct medical computed tomography (CT) images with important details and features. Reconstructed images always suffer from noise and artifact pollution because the acquired projection data may be insufficient or undersampled. In reality, some “isolated noise points” (similar to impulse noise) always exist in low‐dose CT projection measurements. Statistical iterative reconstruction (SIR) methods have shown greater potential to significantly reduce quantum noise but still maintain the image quality of reconstructions than the conventional filtered back‐projection (FBP) reconstruction algorithm. Although the typical total variation‐based SIR algorithms can obtain reconstructed images of relatively good quality, noticeable patchy artifacts are still unavoidable. To address such problems as impulse‐noise pollution and patchy‐artifact pollution, this work, for the first time, proposes a joint regularization constrained SIR algorithm for sparse‐view CT image reconstruction, named “SIR‐JR” for simplicity. The new joint regularization consists of two components: total generalized variation, which could process images with many directional features and yield high‐order smoothness, and the neighborhood median prior, which is a powerful filtering tool for impulse noise. Subsequently, a new alternating iterative algorithm is utilized to solve the objective function. Experiments on different head phantoms show that the obtained reconstruction images are of superior quality and that the presented method is feasible and effective.

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Non-local means theory based Perona–Malik model for image denosing

Cited by 39 publications

References 22 publications

Efficient diffusion coefficient for image denoising

Efficient diffusion coefficient for image denoising

Automatic measurement of rotation center for laminography scanning system without dedicated phantoms

Sparse‐view statistical iterative head CT image reconstruction via joint regularization

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