Quantitative evaluation of the image quality using the fast nonlocal means denoising approach in diffusion-weighted magnetic resonance imaging with high b-value

Park, Jae-Young; Kang, Chang-Ki; Lee, Youngjin

doi:10.1007/s40042-020-00028-4

Cited by 5 publications

(1 citation statement)

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“…With the continuous development of technology, human access to information is obtained through human vision, hearing and touch, and other senses, the vast majority of which information is derived from human vision. And in real life, image acquisition is vulnerable to external interference to form noisy images, and in the process of image segmentation and parameter estimation of noisy images, it will cause errors in the resulting image so that the image denoising processing will become the current research hot spot in the field of image processing [ 1 – 5 ]. In 1992, Donoho proposed the small wave threshold atrophy method; the algorithm with its own denoise superiority quickly attracted people's attention, but its tendency to “overstrangling” wavelet coefficient and cannot optimally represent the line and surface singularity in the image, so that the wavelet transformation in the image denoising has certain limitations [ 6 , 7 ], but denoising in the past, there is still the problem of fidelity.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Learning Image Denoising Algorithm Based on Eigenvalue Extraction and the GAN Model

Feng

et al. 2022

Computational Intelligence and Neuroscience

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This paper proposes a self-adjusting generative confrontation network image denoising algorithm. The algorithm combines noise reduction and the adaptive learning GAN model. First, the algorithm uses image features to preprocess the image and extract the effective information of the image. Then, the edge signal is classified according to the threshold value to suppress the problem of “excessive strangulation,” and then the edge signal of the image is extracted to enhance the effective signal in the high-frequency signal. Finally, the algorithm uses an adaptive learning GAN model to further train the image. Each iteration of the generator network is composed of three stages. And then, we get the best value. Through experiments, it can be seen from the data that the article algorithm is compared with the traditional algorithm and the literature algorithm. Under the same conditions, the algorithm can ensure the operating efficiency while having better fidelity, and it can still denoise at the same time. The edge signal of the image is preserved and has a better visual effect.

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Section: Introductionmentioning

confidence: 99%