CT Image Super Resolution Based On Improved SRGAN

Jiang, Xue-Yin; Xu, Y.; Wei, Pingping; Zhou, Zhuming

doi:10.1109/icccs49078.2020.9118497

Cited by 11 publications

(2 citation statements)

References 10 publications

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“…The generator network of SRGAN is an improvement on SR-ResNet (super-resolution residual network) [11], which mainly includes four components: low-level feature extraction, high-level feature extraction, de-convolutional layer and CNN reconstruction layer. The input to the generator network is a low-resolution image, which first passes through a 9×9 convolutional layer and a relu-activated layer.…”

Section: Image Preprocessing With Srganmentioning

confidence: 99%

YOLOv5s-MEE: A YOLOv5-based Algorithm for Abnormal Behavior Detection in Central Control Room

Yuan,

Fan,

Zhang

2024

ITC

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Aiming to quickly and accurately detect abnormal behaviors of workers in central control rooms, such as playing mobile phone and sleeping, an abnormal behavior detection algorithm based on improved YOLOv5 is proposed. The technique uses SRGAN to reconstruct the input image to improve the resolution and enhance the detailed information. Then, the MnasNet is introduced to replace the backbone feature extraction network of the original YOLOv5, which could achieve the lightweight of the model. Moreover, the detection accuracy of the whole network is enhanced by adding the ECA-Net attention mechanism into the feature fusion network structure of YOLOv5 and modifying the loss function as EIOU. The experimental results in the custom dataset show that compared with the original YOLOv5 algorithm, the algorithm proposed in this paper improves thedetection speed to 75.50 frames/s under the condition of high detection accuracy, which meets the requirements of real-time detection. Meanwhile, compared with other mainstream behavior detection algorithms, this algorithm also shows better detection performance.

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Section: Image Preprocessing With Srganmentioning

confidence: 99%

YOLOv5s-MEE: A YOLOv5-based Algorithm for Abnormal Behavior Detection in Central Control Room

Yuan,

Fan,

Zhang

2024

ITC

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“…In medical practices, computerized tomography (CT) images are used for clinical diagnosis, but hardware limitations and time constraints cause CT images in real scenes to have low resolution. These images are upsampled in [ 19 ] using SRGANs, which outperformed state-of-the-art baseline methods.…”

Section: Related Workmentioning

confidence: 99%

Super Resolution Generative Adversarial Network (SRGANs) for Wheat Stripe Rust Classification

Maqsood

Mumtaz

Haq

et al. 2021

Sensors

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Wheat yellow rust is a common agricultural disease that affects the crop every year across the world. The disease not only negatively impacts the quality of the yield but the quantity as well, which results in adverse impact on economy and food supply. It is highly desired to develop methods for fast and accurate detection of yellow rust in wheat crop; however, high-resolution images are not always available which hinders the ability of trained models in detection tasks. The approach presented in this study harnesses the power of super-resolution generative adversarial networks (SRGAN) for upsampling the images before using them to train deep learning models for the detection of wheat yellow rust. After preprocessing the data for noise removal, SRGANs are used for upsampling the images to increase their resolution which helps convolutional neural network (CNN) in learning high-quality features during training. This study empirically shows that SRGANs can be used effectively to improve the quality of images and produce significantly better results when compared with models trained using low-resolution images. This is evident from the results obtained on upsampled images, i.e., 83% of overall test accuracy, which are substantially better than the overall test accuracy achieved for low-resolution images, i.e., 75%. The proposed approach can be used in other real-world scenarios where images are of low resolution due to the unavailability of high-resolution camera in edge devices.

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Super-Resolution Reconstruction of CT Images Based on Multi-scale Information Fused Generative Adversarial Networks

Liu,

Su,

et al. 2023

Ann Biomed Eng

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CT Image Super Resolution Based On Improved SRGAN

Cited by 11 publications

References 10 publications

YOLOv5s-MEE: A YOLOv5-based Algorithm for Abnormal Behavior Detection in Central Control Room

YOLOv5s-MEE: A YOLOv5-based Algorithm for Abnormal Behavior Detection in Central Control Room

Super Resolution Generative Adversarial Network (SRGANs) for Wheat Stripe Rust Classification

Super-Resolution Reconstruction of CT Images Based on Multi-scale Information Fused Generative Adversarial Networks

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