Analysis of BM3D Denoising Techniques to Improvement of Thoracal MRI Image Quality; Study on Low Field MRI

Astari, Fisnandya Meita; Mulyantoro, Donny Kristanto; Indrati, Rini

doi:10.1016/j.jmir.2022.10.081

Cited by 2 publications

(1 citation statement)

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“…The BM3D consists of two stages, basic estimation filtering and final estimation filtering [32][33][34][35], which utilize the similarity between image blocks for noise reduction. The basic estimation stage consists of block matching grouping, 3D collaborative hard threshold filtering, and aggregated weighted filtering.…”

Section: Generalized Anscombe Transformmentioning

confidence: 99%

Expanded Scene Image Preprocessing Method for the Shack–Hartmann Wavefront Sensor

Chen,

Jia,

Zhou

et al. 2023

Applied Sciences

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Due to the influence of atmospheric turbulence, the detector, and background noise, the subaperture image of an extended scene Shack–Hartmann wavefront sensor will have a low signal-to-noise ratio, which will introduce errors to the offset estimation and reduce the accuracy of the slope measurement. To solve this problem, this paper proposes a cross-correlation subaperture image preprocessing method, which uses the generalized Anscombe transform to convert the Gauss–Poisson noise into Gaussian noise and introduces residual feedback on the basis of BM3D to achieve the efficient denoising of subaperture images. The simulation results show that compared with the three commonly used denoising algorithms, the proposed method improves the relative error of the subaperture offset calculation by 51.96% and the corresponding Zernike coefficient of distorted reconstruction wavefront by 85.56%, which realizes the improvement in the detection accuracy on the basis of effectively retaining image details.

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Section: Generalized Anscombe Transformmentioning

confidence: 99%

Expanded Scene Image Preprocessing Method for the Shack–Hartmann Wavefront Sensor

Chen,

Jia,

Zhou

et al. 2023

Applied Sciences

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AsymUNet: An Efficient Multi-Layer Perceptron Model Based on Asymmetric U-Net for Medical Image Noise Removal

Cui,

Hong,

Yang

et al. 2024

Electronics

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With the continuous advancement of deep learning technology, U-Net–based algorithms for image denoising play a crucial role in medical image processing. However, most U-Net-based medical image denoising algorithms typically have large parameter sizes, which poses significant limitations in practical applications where computational resources are limited or large-scale patient data processing are required. In this paper, we propose a medical image denoising algorithm called AsymUNet, developed using an asymmetric U-Net framework and a spatially rearranged multilayer perceptron (MLP). AsymUNet utilizes an asymmetric U-Net to reduce the computational burden, while a multiscale feature fusion module enhances the feature interaction between the encoder and decoder. To better preserve the image details, spatially rearranged MLP blocks serve as the core building blocks of AsymUNet. These blocks effectively extract both the local and global features of the image, reducing the model’s reliance on prior knowledge of the image and further accelerating the training and inference processes. Experimental results demonstrate that AsymUNet achieves superior performance metrics and visual results compared with other state-of-the-art methods.

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Analysis of BM3D Denoising Techniques to Improvement of Thoracal MRI Image Quality; Study on Low Field MRI

Cited by 2 publications

References 0 publications

Expanded Scene Image Preprocessing Method for the Shack–Hartmann Wavefront Sensor

Expanded Scene Image Preprocessing Method for the Shack–Hartmann Wavefront Sensor

AsymUNet: An Efficient Multi-Layer Perceptron Model Based on Asymmetric U-Net for Medical Image Noise Removal

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