Adaptive Gaussian notch filter for removing periodic noise from digital images

Varghese, Justin; Subhash, Saudia; Subramaniam, Kamalraj; Sridhar, Kandi

doi:10.1049/iet-ipr.2018.5707

Cited by 12 publications

(6 citation statements)

References 29 publications

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“…We have not presented the results for the removal of the Moire artifacts using the adaptive Gaussian notch filter because Moire artifacts were not generated in our results. However, Varghese et al [ 50 ] clearly demonstrate the removal of Moire artifacts using an adaptive Gaussian notch filter (Fig. 7 of the cited paper).…”

Section: Discussionmentioning

confidence: 99%

“…The notch adaptive Gaussian filter [ 50 ] proposed in our study can segment and extract periodic noise by adaptively analyzing the average spatial frequency from a change in an adjacent signal. Consequently, this analysis enables the effective elimination of periodic noise by changing the application size of the notch filter.…”

Section: Methods and Experimental Setupmentioning

confidence: 99%

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Acquisition of a single grid-based phase-contrast X-ray image using instantaneous frequency and noise filtering

2022

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Background To obtain phase-contrast X-ray images, single-grid imaging systems are effective, but Moire artifacts remain a significant issue. The solution for removing Moire artifacts from an image is grid rotation, which can distinguish between these artifacts and sample information within the Fourier space. However, the mechanical movement of grid rotation is slower than the real-time change in Moire artifacts. Thus, Moire artifacts generated during real-time imaging cannot be removed using grid rotation. To overcome this problem, we propose an effective method to obtain phase-contrast X-ray images using instantaneous frequency and noise filtering. Result The proposed phase-contrast X-ray image using instantaneous frequency and noise filtering effectively suppressed noise with Moire patterns. The proposed method also preserved the clear edge of the inner and outer boundaries and internal anatomical information from the biological sample, outperforming conventional Fourier analysis-based methods, including absorption, scattering, and phase-contrast X-ray images. In particular, when comparing the phase information for the proposed method with the x-axis gradient image from the absorption image, the proposed method correctly distinguished two different types of soft tissue and the detailed information, while the latter method did not. Conclusion This study successfully achieved a significant improvement in image quality for phase-contrast X-ray images using instantaneous frequency and noise filtering. This study can provide a foundation for real-time bio-imaging research using three-dimensional computed tomography.

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Section: Discussionmentioning

confidence: 99%

Section: Methods and Experimental Setupmentioning

confidence: 99%

Acquisition of a single grid-based phase-contrast X-ray image using instantaneous frequency and noise filtering

2022

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“…Otherwise, the filter is applied to the corrupted image patch to produce the restored data as an outputis depicted in Fig. 2 (Varghese et al 2020).…”

Section: Adaptive Gaussian Notch Filtermentioning

confidence: 99%

A novel approach for predicting the tc center of remotely sensed images using pso based density matrix

Mubeen¹,

Priya²,

Vijayaraj³

2021

Earth Sci Inform

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In the present work,an image extraction technique has been designed to fix the center spot of the tropical cyclone. The cyclone prediction is processed within the CBIR system and hence it established a well-effective output. A raw cyclone image is being processed under the image processing techniques such as Adaptive Gaussian Notch Filter (Pre-processing), Adaptive Thresholding (segmentation) and Feature-extraction. The extracted features are collectively used as an input to the CNN-HMO classifier which produces a relevant classified output. Relevance feedback is a key factor which passes these corresponding outputs to the user. User selects the required cyclone image after then, the image extraction process of PSO based density matrix takes place. The extractor has variance, gradient and density matrix (DM) to predit the center spot. So that the brightness temperature is computed for each pixel coordinates in both zonal and meridional axial direction. After then DM point converges over every pixel in an image and a PSO is followed towards the pixel orientation of each convergence line to determine the best optimal solution (i.e.,) the center of the Tropical Cyclone. With this optimizing criterion, the center of the Tropical Cyclone is generated.

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“…In the process of image acquisition, encoding, storage and transmission, all the images are "dirtied" by visible or invisible noise that may degrade the image [1,2]. The image noise mostly includes impulse (salt and pepper) noise, Gaussian noise and mixed Noise etc.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Gaussian Filter Based on Gaussian Symmetric Markov Random Field

Xiong

Zhang

et al. 2022

IEEE Access

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This article presents a new image denoising algorithm that uses Gaussian Symmetric Markov random fields based on maximum a posteriori estimation. First, an image denoising model based on Gaussian Symmetric Markov random fields is built, and the image denoising problem was converted to a maximum a posteriori estimation problem. The prior probability of an image can be estimated using the Gibbs distribution, which is equivalent to Markov random fields. Second, the maximum a posteriori estimation is calculated using the expectation-maximization algorithm and conjugate gradient method, where the expectation-maximization algorithm is used to estimate Gaussian Symmetric Markov random field hyper-parameters and the conjugate gradient method is used to calculate the criterion function. The experimental results for the synthetic images and the standard Berkeley segmentation datasets demonstrate the success of the proposed Gaussian denoising filter, as compared with the state-of-the-art methods such as BM3D, WNNM, SGWD-HMMs, SSLBD, DnCNN and BUIFD.INDEX TERMS Gaussian Markov random fields, expectation-maximization, conjugate gradient, denoising filter.

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Adaptive Gaussian notch filter for removing periodic noise from digital images

Cited by 12 publications

References 29 publications

Acquisition of a single grid-based phase-contrast X-ray image using instantaneous frequency and noise filtering

Acquisition of a single grid-based phase-contrast X-ray image using instantaneous frequency and noise filtering

A novel approach for predicting the tc center of remotely sensed images using pso based density matrix

An Efficient Gaussian Filter Based on Gaussian Symmetric Markov Random Field

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