Efficient medical image enhancement based on CNN‐FBB model

Qiu, Tao; Wen, Chang; Xie, Kai; Wen, Fangqing; Sheng, Guanqun; Tang, Xulong

doi:10.1049/iet-ipr.2018.6380

Cited by 37 publications

(23 citation statements)

References 45 publications

(49 reference statements)

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“…We start training with generators ( g ) and discriminators ( d ) with low spatial resolution, with a resolution of 4 × 4 at the beginning, and then add a convolution layer to G and D after each training, thus as to gradually improve the spatial resolution of the generated signal [ 32 , 33 , 34 , 35 ]. All involved convolution layers can be retrained in the whole training process.…”

Section: Weak Signal Reconstruction Methods Under Emdnn Modelmentioning

confidence: 99%

Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

Chen

Xie

Wen

et al. 2020

Sensors

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In order to enhance weak signals in strong noise background, a weak signal enhancement method based on EMDNN (neural network-assisted empirical mode decomposition) is proposed. This method combines CEEMD (complementary ensemble empirical mode decomposition), GAN (generative adversarial networks) and LSTM (long short-term memory), it enhances the efficiency of selecting effective natural mode components in empirical mode decomposition, thus the SNR (signal-noise ratio) is improved. It can also reconstruct and enhance weak signals. The experimental results show that the SNR of this method is improved from 4.1 to 6.2, and the weak signal is clearly recovered.

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Section: Weak Signal Reconstruction Methods Under Emdnn Modelmentioning

confidence: 99%

Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

Chen

Xie

Wen

et al. 2020

Sensors

Self Cite

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“…The mean filter is equivalent to a low-pass filter. Although the operation is simple and the calculation speed is fast, the mean filter will lose the details in the denoising process and make the image blurry [ 15 ]. Median filtering was originally a nonlinear processing technique used to analyze time series and was later used to remove salt and pepper noise.…”

Section: Related Researchmentioning

confidence: 99%

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

Yao

2020

Computational and Mathematical Methods in Medicine

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In order to improve the resolution of magnetic resonance (MR) image and reduce the interference of noise, a multifeature extraction denoising algorithm based on a deep residual network is proposed. First, the feature extraction layer is constructed by combining three different sizes of convolution kernels, which are used to obtain multiple shallow features for fusion and increase the network’s multiscale perception ability. Then, it combines batch normalization and residual learning technology to accelerate and optimize the deep network, while solving the problem of internal covariate transfer in deep learning. Finally, the joint loss function is defined by combining the perceptual loss and the traditional mean square error loss. When the network is trained, it can not only be compared at the pixel level but also be learned at a higher level of semantic features to generate a clearer target image. Based on the MATLAB simulation platform, the TCGA-GBM and CH-GBM datasets are used to experimentally demonstrate the proposed algorithm. The results show that when the image size is set to 190 × 215 and the optimization algorithm is Adam, the performance of the proposed algorithm is the best, and its denoising effect is significantly better than other comparison algorithms. Especially under high-intensity noise levels, the denoising advantage is more prominent.

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“…Image enhancement [1][2][3][4] is a major research hotspot of interest within the various computer vision tasks [5,6]. Very often, partial darkness or even global darkness are likely to exist in photos taken by the camera for some reasons, such as weak light, low exposure and backlight.…”

Section: Introductionmentioning

confidence: 99%

AMBCR: Low‐light image enhancement via attention guided multi‐branch construction and Retinex theory

Zhou

Nie

et al. 2021

IET Image Processing

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Due to different lighting environments and equipment limitations, low-light images have high noise, low contrast and unobvious colours. The main purpose of low-light image enhancement is to preserve the details and suppress noise as much as possible while improving the contrast of the image. Here, different networks are first combined to construct a multi-branch module for features extraction, and use the module and Retinex theory to extract the reflection map of the image. Then an attention mechanism is introduced into the multi-branch construction to balance the feature weight of each branch, and get the final result by the reconstruction module. The Retinex theory is used to calculate the L 1 loss and the gradient loss for the intermediate feature map of the entire model to train our framework. The entire process is completed in an end-to-end-way, which avoids the handcrafted reconstruction rules and reduces the workload. What's more, a large number of experiments demonstrate that the proposed framework performs better results than stateof-the-art algorithms in both quantitative and qualitative evaluations of image enhancement.

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Efficient medical image enhancement based on CNN‐FBB model

Cited by 37 publications

References 45 publications

Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

AMBCR: Low‐light image enhancement via attention guided multi‐branch construction and Retinex theory

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