An adaptive diffusion coefficient selection for image denoising

Rafsanjani, Hossein Khodabakhshi; Sedaaghi, Mohammad Hossein; Saryazdi, Saeı̈d

doi:10.1016/j.dsp.2017.02.004

Cited by 49 publications

(11 citation statements)

References 38 publications

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“…Residual image (also named method noise) is defined as the difference between the noisy image and its filtered version, thus it contains high frequency information of details lost in filtering. Many studies have utilized the high frequency information in residual image to remove noise and meanwhile to preserve image details [48]- [50]. In this paper, we combine image local variance, residual local variance, and image gradient to construct the diffusion coefficient function, which is more effective to control diffusion degree in edge/detail regions that are contaminated with noise/artifacts.…”

Section: Methodsmentioning

confidence: 99%

Artifact Suppressed Nonlinear Diffusion Filtering for Low-Dose CT Image Processing

Liu

Chen

et al. 2019

IEEE Access

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Computed tomography (CT) images with a low-dose protocol generally have severe mottle noise and streak artifacts. In this paper, we propose a novel diffusion method named ''artifact suppressed nonlinear diffusion filtering (ASNDF),'' to process low-dose CT (LDCT) images. Different from other diffusion filtering methods, the proposed ASNDF not only includes image gradient as the main cue to construct a diffusion coefficient function, but also incorporates the local variances of image to be diffused and residual image between two adjacent diffusions. In detail, the classical PM diffusion is first performed to get the initial residual image, and then from the second iteration, the LDCT image is processed according to the ASNDF processing. Simulated data, clinical data and rat data are conducted to evaluate the proposed method, and the comparison experiments with other competing methods show that the proposed ASNDF method makes an improvement in artifact suppression and structure preservation, and offers a sound alternative to process LDCT images from most current CT systems.

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Section: Methodsmentioning

confidence: 99%

Artifact Suppressed Nonlinear Diffusion Filtering for Low-Dose CT Image Processing

Liu

Chen

et al. 2019

IEEE Access

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“…Additionally, a new algorithm was developed by H.K. Rafsanjani, et al [21] to select the diffusion coefficients using the gradient magnitude and residual local power. The developed algorithm effectively preserved the image details like edges and textures, because the texture region corresponds to the value of local power residue.…”

Section: Literature Surveymentioning

confidence: 99%

Robust Gaussian Noise Detection and Removal in Color Images using Modified Fuzzy Set Filter

Suneetha¹,

Reddy

2020

Journal of Intelligent Systems

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In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques.

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“…Rafsanjani et al [9] proposed an algorithm that adaptively chooses diffusion coefficient using the residual local power and the number of the gradient magnitude. In this algorithm, a texture detector operator was introduced by applying Perona and Malik (PM) process to the noisy image and computing the local variations of residue.…”

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 adaptive diffusion coefficient selection for image denoising

Cited by 49 publications

References 38 publications

Artifact Suppressed Nonlinear Diffusion Filtering for Low-Dose CT Image Processing

Artifact Suppressed Nonlinear Diffusion Filtering for Low-Dose CT Image Processing

Robust Gaussian Noise Detection and Removal in Color Images using Modified Fuzzy Set Filter

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

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