FPGA‐based favourite skin colour restoration using improved histogram equalization with variable enhancement degree and ensemble extreme learning machine

Swain, Bhanja Kishor; Dash, P.K.

doi:10.1049/ipr2.12101

Cited by 6 publications

(3 citation statements)

References 37 publications

(51 reference statements)

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“…The confidence vector data of multiple image transformation levels are obtained by machine learning algorithm [21,22], and then, the proportion of image compression parameters is obtained by secondary classification. After the parameter data of each part of the image is detected, analyzed, recorded, and sorted out, it is saved in the data manager of the image equalization and color restoration [23,24] program of the image processing system to lay the data foundation for the process of mural digital image equalization and color restoration.…”

Section: Mural Digital Image Restoration Based Onmentioning

confidence: 99%

Intelligent Restoration Technology of Mural Digital Image Based on Machine Learning Algorithm

Zhou

Xie

2022

Wireless Communications and Mobile Computing

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Influenced by the natural environment and preservation conditions, the frescoes in the monastery were damaged to some extent. In order to solve the fresco image restoration conversion and structural decomposition defects, an intelligent fresco digital image restoration technique based on machine learning algorithm is proposed. Firstly, the digital image information of mural is collected by a scanning program, and the data in the database is extracted. The mean filter template is used to restore the color of mural digital image, and the Gaussian template is used to restore the color of image details. The two are superimposed to ensure that the image is not affected by distortion and halo, so as to ensure the clarity of image and boundary. The depth learning model of local fuzzy feature restoration of mural digital image is obtained by using multimodal feature decomposition method, and the intelligent restoration of mural digital image is realized by this model. The experimental results show that this method can effectively repair the local fuzzy features of mural digital image. The repair precision is more than 95.7% and fills in the missing information from the image, and the image quality is good.

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Section: Mural Digital Image Restoration Based Onmentioning

confidence: 99%

Intelligent Restoration Technology of Mural Digital Image Based on Machine Learning Algorithm

Zhou

Xie

2022

Wireless Communications and Mobile Computing

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“…Generally speaking, convolutional layers are meant to extract features while fully-connected layers are used for classification. Each convolutional layer was followed by activation function and a stochastic pooling layer [23,24]. Activation function was applied for nonlinear transformation after convolution calculation.…”

Section: Structure Of Our Six-layer Cnnmentioning

confidence: 99%

Multiple Sclerosis Detection via 6-layer Stochastic Pooling Convolutional Neural Network and Multiple-way Data Augmentation

Wang

Lima

2021

STEMedicine

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Multiple sclerosis is one of most widespread autoimmune neuroinflammatory diseases which mainly damages body function such as movement, sensation, and vision. Despite of conventional clinical presentation, brain magnetic resonance imaging of white matter lesions is often applied to diagnose multiple sclerosis at the early stage. In this article, we proposed a 6-layer stochastic pooling convolutional neural network with multiple-way data augmentation for multiple sclerosis detection in brain MRI images. Our approach does not demand hand-crafted features unlike those traditional machine learning methods. Via application of stochastic pooling and multiple-way data augmentation, our 6-layer CNN achieved equivalent performance against those deep learning methods which consist of so many layers and parameters that ordinarily bring difficulty to training. The results showed that this 6-layer CNN obtained a sensitivity of 95.98±0.46%, a specificity of 95.67±0.92%, and an accuracy of 95.82±0.58%. According to comparison experiments, our results are better than state-of-the-art approaches. Further, we also conducted ablation experiments to examine the contribution of stochastic pooling and multiple-way data augmentation to the original CNN model. The contrast experiments revealed that our scheme of stochastic pooling and multiple-way data augmentation enhanced the original 6-layer CNN model compared to those using maximum pooling or average pooling and inadequate data augmentation.

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“…At this time, the pooling layer is used to reduce the dimension of the feature map [32]. There are two advantages for pooling layers: (i) it can make the feature graph smaller, simplify the calculation complexity of the network, reduce the parameters and calculations, and prevent over fitting [33]; (ii) the receptive field can be increased by compressing features, extracting features and retaining main features, and keeping scale invariance.…”

Section: Pooling Layermentioning

confidence: 99%

Alcoholic brain injury via 8-layer customized deep convolution neural network

Zhu

Brown

2021

STEMedicine

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Alcohol can act quickly in the human body and alter mood and behavior. If alcohol is consumed in excess, it will accumulate in the organs of the body, especially in the liver and brain. To a certain extent, the symptoms of alcoholism will appear. So far, the main method of diagnosis of alcoholic brain injury is through MRI images by radiologists. However, this is a very subjective diagnosis. Radiologists may be affected by external factors, such as physical discomfort, lack of rest, inattention, etc., resulting in diagnostic errors. In this paper, we proposed a novel 8-layer customized deep convolution neural network for alcoholic brain injury detection, which contains five convolution layers, five pooling layers, and three fully connected layers. There are three improvements in this paper, (i) Based on deep learning, we proposed a method for automatic diagnosis of alcoholic brain injury; (ii) We introduced Dropout to the proposed structure to improve robustness; (iii) Compared with other state-of-the-art approaches, the proposed structure is more efficient. The experimental results showed that the sensitivity, specificity, precision, accuracy, F1, MCC and FMI were 96.14±1.99, 96.20±1.47, 95.98±1.54, 96.17±1.55, 96.05±1.62, 93.34±3.11, 96.06±1.62 respectively. According to comparison results, our method performed the best. The proposed model is effective in detecting alcoholic brain injury based on MRI images.

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FPGA‐based favourite skin colour restoration using improved histogram equalization with variable enhancement degree and ensemble extreme learning machine

Cited by 6 publications

References 37 publications

Intelligent Restoration Technology of Mural Digital Image Based on Machine Learning Algorithm

Intelligent Restoration Technology of Mural Digital Image Based on Machine Learning Algorithm

Multiple Sclerosis Detection via 6-layer Stochastic Pooling Convolutional Neural Network and Multiple-way Data Augmentation

Alcoholic brain injury via 8-layer customized deep convolution neural network

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