A Study of Adaptive Fractional-Order Total Variational Medical Image Denoising

Zhang, Yanzhu; Liu, Tingting; Yang, Fan; Yang, Qi

doi:10.3390/fractalfract6090508

Cited by 8 publications

(6 citation statements)

References 37 publications

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“…To improve the learning ability of the neural network and enable the network to mine more abstract features that are close to the essence of the image, a total of two double residuals blocks are designed in this chapter, and the network structure is up to 14 layers. 22 The double residuals structure can not only accelerate network training but also superimpose and fuse the features extracted from the front and back layers of the network, to transfer the feature information continuously, which is conducive to the preservation of image information, especially the preservation of details. In neural networks, the double residual structure is a potent architectural design that improves information flow, expedites training by addressing the vanishing gradient issue, and allows feature fusion through skip connections.…”

Section: Residual Network Designmentioning

confidence: 99%

“…Across an array of datasets, utilizing the normal distribution is an effective strategy for producing reliable and precise models. Inspired by double residuals, the residual block structure of this article is designed, but different from it, the structure of double residuals block with five convolutional layers is designed in this chapter. To improve the learning ability of the neural network and enable the network to mine more abstract features that are close to the essence of the image, a total of two double residuals blocks are designed in this chapter, and the network structure is up to 14 layers 22 . The double residuals structure can not only accelerate network training but also superimpose and fuse the features extracted from the front and back layers of the network, to transfer the feature information continuously, which is conducive to the preservation of image information, especially the preservation of details.…”

Section: Medical Image Denoising By Convolutional Neural Network Base...mentioning

confidence: 99%

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Research on denoising and enhancing methods of medical images based on convolutional neural networks

Beibei

2024

Concurrency and Computation

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SummaryIn the process of modern medical diagnosis, medical image‐assisted diagnosis plays a very important role. However, the process of medical image acquisition, will be affected by various types and degrees of noise, and there will be a certain probability of producing strip artifacts, which will interfere with the doctor's diagnosis, analysis, and treatment of diseases to a certain extent. However, the traditional medical image denoising method will cause problems such as image edge blurring and detail loss, and it is difficult to achieve the balance between noise removal and detail information retention. Therefore, denoising medical images and improving the accuracy of denoising as much as possible have very important scientific research significance and clinical application value. Based on this, this article proposes a medical image denoising method based on a double residual convolutional neural network and compares it with traditional medical images denoising methods such as K‐SVD, BM3D, and PNLM3. Experimental results show that the medical image denoising method based on the double residual convolutional neural network proposed in this article has excellent performance.

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Section: Residual Network Designmentioning

confidence: 99%

Section: Medical Image Denoising By Convolutional Neural Network Base...mentioning

confidence: 99%

Research on denoising and enhancing methods of medical images based on convolutional neural networks

Beibei

2024

Concurrency and Computation

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“…However, its application in the field of medical image denoising is relatively limited, and there is insufficient research on parameter selection and algorithm convergence. In practical clinical imaging, noise occurrence may be influenced by factors such as equipment type, imaging mode, and processing workflow 6 . In-depth analysis of these factors will help better understand the characteristics and sources of noise in medical images, providing strong support for proposing rational image denoising techniques.…”

Section: Introductionmentioning

confidence: 99%

Research on improved black widow algorithm for medical image denoising

Qu,

Liu,

Zhang

2024

Sci Rep

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Improving the quality of medical images is crucial for accurate clinical diagnosis; however, medical images are often disrupted by various types of noise, posing challenges to the reliability and diagnostic accuracy of the images. This study aims to enhance the Black Widow optimization algorithm and apply it to the task of denoising medical images to improve both the quality of medical images and the accuracy of diagnostic results. By introducing Tent mapping, we refined the Black Widow optimization algorithm to better adapt to the complex features of medical images. The algorithm's denoising capabilities for various types of noise were enhanced through the combination of multiple filters, all without the need for training each time to achieve preset goals. Simulation results, based on processing a dataset containing 1588 images with Gaussian, salt-and-pepper, Poisson, and speckle noise, demonstrated a reduction in Mean Squared Error (MSE) by 0.439, an increase in Peak Signal-to-Noise Ratio (PSNR) by 4.315, an improvement in Structural Similarity Index (SSIM) by 0.132, an enhancement in Edge-to-Noise Ratio (ENL) by 0.402, and an increase in Edge Preservation Index (EPI) by 0.614. Simulation experiments verified that the proposed algorithm has a certain advantage in terms of computational efficiency. The improvement, incorporating Tent mapping and a combination of multiple filters, successfully elevated the performance of the Black Widow algorithm in medical image denoising, providing an effective solution for enhancing medical image quality and diagnostic accuracy.

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“…Image processing has been widely adopted in various fields [3][4][5][6][7][8][9][10][11]. In addition, recently, deep learning methods have offered various solutions, and the use of computer vision has grown significantly in various applications including building monitoring, image enhancement, medical image processing, biomedical engineering, and underwater computer vision, where some research has adopted fractal-related perspectives, also in [9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Crop and Weed Segmentation and Fractal Dimension Estimation Using Small Training Data in Heterogeneous Data Environment

Akram,

Hong,

Kim

et al. 2024

Fractal Fract

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The segmentation of crops and weeds from camera-captured images is a demanding research area for advancing agricultural and smart farming systems. Previously, the segmentation of crops and weeds was conducted within a homogeneous data environment where training and testing data were from the same database. However, in the real-world application of advancing agricultural and smart farming systems, it is often the case of a heterogeneous data environment where a system trained with one database should be used for testing with a different database without additional training. This study pioneers the use of heterogeneous data for crop and weed segmentation, addressing the issue of degraded accuracy. Through adjusting the mean and standard deviation, we minimize the variability in pixel value and contrast, enhancing segmentation robustness. Unlike previous methods relying on extensive training data, our approach achieves real-world applicability with just one training sample for deep learning-based semantic segmentation. Moreover, we seamlessly integrated a method for estimating fractal dimensions into our system, incorporating it as an end-to-end task to provide important information on the distributional characteristics of crops and weeds. We evaluated our framework using the BoniRob dataset and the CWFID. When trained with the BoniRob dataset and tested with the CWFID, we obtained a mean intersection of union (mIoU) of 62% and an F1-score of 75.2%. Furthermore, when trained with the CWFID and tested with the BoniRob dataset, we obtained an mIoU of 63.7% and an F1-score of 74.3%. We confirmed that these values are higher than those obtained by state-of-the-art methods.

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A Study of Adaptive Fractional-Order Total Variational Medical Image Denoising

Cited by 8 publications

References 37 publications

Research on denoising and enhancing methods of medical images based on convolutional neural networks

Research on denoising and enhancing methods of medical images based on convolutional neural networks

Research on improved black widow algorithm for medical image denoising

Crop and Weed Segmentation and Fractal Dimension Estimation Using Small Training Data in Heterogeneous Data Environment

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