An improved anisotropic diffusion filter with semi-adaptive threshold for edge preservation

Xu, Jiangtao; Jia, Yuanyuan; Shi, Zaifeng; Pang, Ke

doi:10.1016/j.sigpro.2015.07.017

Cited by 69 publications

(22 citation statements)

References 22 publications

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“…We compare the proposed method with PM [7], ROF [15], Complex diffusion [19], Barbu et al [20], Xiaoli et al [21], WWBAD [22], and Xu et al [23] methods using seven criteria: PSNR, MSSIM, UQI, VIF, FSIM, IW-SSIM, and visual quality of denoised images. Fig.…”

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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“…In this filter, at every point, local image variation is calculated and neighbourhood pixel values are averaged. JiangtaoXu et al [19], proposed an edge-preserving diffusion method for suppressing Gaussian noise using anisotropic diffusion filter [AD] with semi adaptive threshold. This technique overcomes the blurring edges and lines of an image and improves the ability to suppress noise when the noise level is high.…”

Section: Fig 6 Calculating the Median Value Of A Pixel Neighborhood mentioning

confidence: 99%

An Efficient Noise Separation Technique for Removal of Gaussian and Mixed Noises in Monochrome and Color Images

2019

IJITEE

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Images are often affected by different kinds of noise while acquiring, storing and transmitting it. Even the datasets gathered by the various image acquiring devices would be contaminated by noise. Hence, there is a need for noise reduction in the image, often called Image De-noising and thereby it becomes the significant concerns and fundamental step in the area of image processing. During image de-noising, the big challenge before the researchers is removing noise from the original image in such a way that most significant properties like edges, lines, etc., of the image, should be preserved. There were various published algorithms and techniques to de-noise the image and every single approach has its own limitations, benefits, and assumptions. This paper reviews the noise models and presents a comparative analysis of various de-noising filters that works for color images with single and mixed noises. It also suggests the best filter for color that involve in producing a high-quality color image. The metrics like PSNR, Entropy, SSIM, MSE, FSIM, and EPI are considered as image quality assessment metrics.

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“…Xu et al [8] proposed a noise removal technique based on a semi-adaptive threshold in anisotropic diffusion filter for achieving better detail information preservation and stronger noise suppressing ability. In this technique, a method of local variance rate was applied for differentiating the corrupted pixels and noise-free pixels.…”

Section: Literature Surveymentioning

confidence: 99%

Image Denoising with Recurrent Polak-Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy Edge-Preserving Segmentation Based on OAMNHA

Indupriya*,

Vijendran

2019

IJITEE

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The most essential process in image processing applications is eliminating the noisy pixels from the images and acquiring the noise-free images with better reconstructed quality. This process has high complexity due to the segmentation of image patches from the noisy images. To simplify this process, Neuro-Fuzzy filtering with Optimum Adaptive parameterized Mask Non-Harmonic Analysis in Curvelet Transform domain (NF-OAMNHA-CT) has been proposed for image denoising. It uses NF edge detector instead of canny edge detector for segmenting and preserving the edge regions including homogeneous texture boundaries with minimized edge distortion. However, it requires gradient information about the error function with respect to the parameter measures during training of NF for optimizing their parameters. Also, it often affects with the fixed local minima. Therefore in this article, Recurrent Polak- Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy (RPCGNF-OAMNHA-CT) technique is proposed to enhance the edge-preserving segmentation in image denoising. At first, the noisy images are defined in the CT domain and fed to the RPCGNF edge detector as edge-preserving segmentation that segments and extracts the edge regions as well as homogeneous texture regions. In this RPCGNF technique, recurrent mechanism is applied to construct PCGNF as an updated edgepreserving segmentation technique that learns their parameters with high efficiency and speeding up the convergence. As a result, the edge regions and homogeneous textures are synchronously segmented as noisy pixels until either the PCGNF converges to the desired accuracy of segmentation or a termination criterion is reached. Further, OAMNHA is applied on each segment to remove the noisy pixels from the images and obtain the noise-free images accurately. Finally, the experimental results illustrate the proposed RPCGNFOAMNHA-CT technique achieves higher efficiency than the NFOAMNHA-CT technique in terms of Peak Signal-to-Noise Ratio (PSNR), Mean Absolute Error (MAE) and Structural Similarity (SSIM).

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An improved anisotropic diffusion filter with semi-adaptive threshold for edge preservation

Cited by 69 publications

References 22 publications

Efficient diffusion coefficient for image denoising

Efficient diffusion coefficient for image denoising

An Efficient Noise Separation Technique for Removal of Gaussian and Mixed Noises in Monochrome and Color Images

Image Denoising with Recurrent Polak-Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy Edge-Preserving Segmentation Based on OAMNHA

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