Digital Image Noise Estimation Using DWT Coefficients

Pimpalkhute, Varad; Page, Rutvik; Kothari, Ashwin; Bhurchandi, Kishor M.; Kamble, Vipin Milind

doi:10.1109/tip.2021.3049961

Cited by 57 publications

(33 citation statements)

References 41 publications

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“…where A represents the length of wavelet coefficients of the multilevel image, α represents the standard deviation of multilevel image noise [10], and T represents the image estimation threshold. In fact, the noise error of the image is unknown, so it is necessary to estimate and calculate the standard deviation of the noise from the noisy signal of the multilevel image; the expression is as follows:…”

Section: Multilevel Image Denoisingmentioning

confidence: 99%

Multilevel Image Edge Detection Algorithm Based on Visual Perception

2022

Security and Communication Networks

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Multilevel image edge repair results directly affect the follow-up image quality evaluation and recognition. Current edge detection algorithms have the problem of unclear edge detection. In order to detect more accurate edge contour information, a multilevel image edge detection algorithm based on visual perception is proposed. Firstly, the digital image is processed by double filtering and fuzzy threshold segmentation; Through the analysis of the contour features of the moving image, the threshold of the moving image features is set, and the latest membership function is obtained to complete the multithreshold optimization. Adaptive smoothing is used to process the contour of the object in the moving image, and the geometric center values of the two adjacent contour points within the contour range are calculated. According to the calculation results, the curvature angle is further calculated, and the curvature symbol is obtained. According to the curvature symbol, the contour features of the moving image are detected. The experimental results show that the proposed algorithm can effectively and accurately detect the edge contour of the image and shorten the reconstruction time, and the detection image resolution is high.

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Section: Multilevel Image Denoisingmentioning

confidence: 99%

Multilevel Image Edge Detection Algorithm Based on Visual Perception

2022

Security and Communication Networks

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“…In Step 1, the standard deviation of noise is evaluated using modified MAD estimator assuming the noise to follow Gaussian distribution. For better estimates, we modify the MAD estimator so that the diagonal elements in the HH sub‐band are eliminated, similar to the algorithm proposed by Pimpalkhute et al 8 In Step 2, the evaluated standard deviation of noise is corrected using the analytical expression that establishes the relationship between the GN and RN thus estimating the nonstationary parameters of noise. It should be noted that most estimation algorithms in the literature, including BURST algorithm, require multiple acquisitions or need additional information about the reconstruction process.…”

Section: Methodsmentioning

confidence: 99%

“…Spatially invariant Rician noise (RN) was widely accepted in literature as an appropriate model of noise in MRI. [6][7][8][9][10][11][12][13][14] The main assumption was a single coil MR acquisition; hence, in all the cases, the noise in the MRI is considered to be spatially homogeneous and hence a single value of noise standard deviation was sufficient to characterize the entire dataset. In the present MRI acquisition scenario, such presumptions certainly fail due to the complexity of fast imaging with multicoil for parallel imaging and reconstruction and local field inhomogeneities.…”

Section: Introductionmentioning

confidence: 99%

“…Spatially invariant Rician noise (RN) was widely accepted in literature as an appropriate model of noise in MRI 6–14 . The main assumption was a single coil MR acquisition; hence, in all the cases, the noise in the MRI is considered to be spatially homogeneous and hence a single value of noise standard deviation was sufficient to characterize the entire dataset.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Nonhomogeneous Noise in 2D Magnetic Resonance Imaging

Shukla

Khandekar

Khaparde

2021

Int J Imaging Syst Tech

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Magnetic resonance images are often influenced by noise. Reliable estimation of noise characteristics is important for image post-processing. The benchmark algorithms often assume a uniform noise model across the image; that is, the noise features are spatially invariant. A few noise estimators are found to cope with nonhomogeneous noise; however, they either require multiple acquisitions or other additional information. In this research work, we develop a method that can accurately estimate the nonhomogeneous parameters of noise from just a single magnitude image. The proposed algorithm is a two-step approach. In the first step, we evaluate the standard deviation of noise assuming that it follows Gaussian distribution. This step utilizes a hybrid of spatial domain edge information suppression and transforms domain coefficient statistics to estimate a single value of standard deviation of noise in magnitude MR images. In the second step, the evaluated noise is corrected using the analytical expression that establishes the relationship between Gaussian noise and Rician noise. Results on synthetic and clinical data evidence the better performance of the proposed algorithm when compared to the benchmark methods.

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“…Wavelet transform [22] has a powerful ability in data analysis and processing, which is developed to overcome the shortcomings of the Fourier transform. It has been successfully applied in many fields, such as signal processing, image processing, pattern recognition, and so on.…”

Section: Wavelet Transformmentioning

confidence: 99%