A LDCT Image Contrast Enhancement Algorithm Based on Single-Scale Retinex Theory

Zhang, Guodong; Sun, Deqing; Yan, Ping; Zhao, Hong; Li, Zhezhu

doi:10.1109/cimca.2008.231

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…For the data augmentation part, the collected CT images were first rotated randomly and flipped horizontally or vertically, and then were resized by center cropping so that the cropped image resolution was reduced from 512 × 512 to 224 × 224. In the field of image enhancement for CT image processing, the Retinex theory was successfully applied by Zhang et al [ 44 ]. Thus, we used the Multi-Scale Retinex (

) enhancement algorithm, which is effective for contrast enhancement and facilitating the observation of details in image lesions [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device

2023

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The rapid spread of coronavirus disease 2019 (COVID-19) has posed enormous challenges to the global public health system. To deal with the COVID-19 pandemic crisis, the more accurate and convenient diagnosis of patients needs to be developed. This paper proposes a deep-learning-based COVID-19 detection method and evaluates its performance on embedded edge-computing devices. By adding an attention module and mixed loss into the original VGG19 model, the method can effectively reduce the parameters of the model and increase the classification accuracy. The improved model was first trained and tested on the PC X86 GPU platform using a large dataset (COVIDx CT-2A) and a medium dataset (integrated CT scan); the weight parameters of the model were reduced by around six times compared to the original model, but it still approximately achieved 98.80%and 97.84% accuracy, outperforming most existing methods. The trained model was subsequently transferred to embedded NVIDIA Jetson devices (TX2, Nano), where it achieved 97% accuracy at a 0.6−1 FPS inference speed using the NVIDIA TensorRT engine. The experimental results demonstrate that the proposed method is practicable and convenient; it can be used on a low-cost medical edge-computing terminal. The source code is available on GitHub for researchers.

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) enhancement algorithm, which is effective for contrast enhancement and facilitating the observation of details in image lesions [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device

2023

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“…The third kernel function (9) computes the summation of weights and intensities in 3 U and 4 U , and the computational complexity is (1) O for this operation. …”

Section: O N B Bmentioning

confidence: 99%

“…x x y y p i p i denotes the neighboring points in the searching window centered at p. The initialization is also set with data (1) (…”

Section: O N B Bmentioning

confidence: 99%

Fast Low-Dose CT Image Processing Using Improved Parallelized Nonlocal Means Filtering

Zhuang¹,

Chen

Hu³

et al. 2014

2014 International Conference on Medical Biometrics

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International audienceAlthough effectively reducing the radiation exposure to patients, low dose CT (LDCT) images are often significantly degraded by severely increased mottled noise/artifacts, which can lead to lowered diagnostic accuracy in clinic. The nonlocal means (NLM) filtering can effectively remove mottled noise/artifacts by utilizing large-scale patch similarity information in LDCT images. But the NLM filtering application in LDCT imaging is also accompanied with high computation cost as a large searching window is often required to include much neighboring information for noise/artifact suppression. To accelerate the NLM filtering and improve its clinical feasibility, we propose in this paper an improved GPUbased parallelization approach. In addition to the straight pixel wise parallelization, the improved parallelization approach exploits the high I/O speed of GPU shared memory. Quantitative experiment demonstrates that significant acceleration is achieved with respect to the traditional pixel-wise parallelizatio

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“…Enhancing low-light images can be classified into conventional techniques and deep learning methods. Conventional techniques include histogram equalization (HE) [3] and Retinex theory methods [4]. Many improved conventional techniques have been developed based on HE and Retinex theory.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modular Network-Based Retinex Fusion Approach for Low-Light Image Enhancement

Wang,

Sun,

Yang

2024

Electronics

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Current low-light image enhancement techniques prioritize increasing image luminance but fail to address issues including loss of intricate distortion of colors and image details. In order to address these issues that has been overlooked by all parties, this paper suggests a multi-module optimization network for enhancing low-light images by integrating deep learning with Retinex theory. First, we create a decomposition network to separate the lighting components and reflections from the low-light image. We incorporated an enhanced global spatial attention (GSA) module into the decomposition network to boost its flexibility and adaptability. This module enhances the extraction of comprehensive information from the image and safeguards against information loss. To increase the illumination component’s luminosity, we subsequently constructed an enhancement network. The Multiscale Guidance Block (MSGB) has been integrated into the improvement network, together with multilayer extended convolution to expand the sensing field and enhance the network’s capability for feature extraction. Our proposed method out-performs existing ways in both objective measures and personal evaluations, emphasizing the virtues of the procedure outlined in this paper.

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A LDCT Image Contrast Enhancement Algorithm Based on Single-Scale Retinex Theory

Cited by 8 publications

References 15 publications

Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device

Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device

Fast Low-Dose CT Image Processing Using Improved Parallelized Nonlocal Means Filtering

Multi-Modular Network-Based Retinex Fusion Approach for Low-Light Image Enhancement

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