Joint Learning of Super-Resolution and Perceptual Image Enhancement for Single Image

Xu, Y.; Li, Li; Sang, Genan; Zhang, Yuewan; Wang, Zhengyang; Wei, Pingping

doi:10.1109/access.2021.3068861

Cited by 2 publications

(1 citation statement)

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“…For medical image generation, researchers have successfully generated realistic medical images, such as skin lesion images and fundus images, using the GAN model [11][12]. These generated images not only have realistic appearance, but also have similar characteristics to real images, which can be used to broaden and expand medical image data.…”

Section: Application Of Deep Learning In Medical Image Reconstruction...mentioning

confidence: 99%

Research and Application of Deep Learning in Medical Image Reconstruction and Enhancement

Gong,

Qiu,

Liu

et al. 2024

FCIS

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In recent years, deep learning technology has made remarkable progress in medical image reconstruction and enhancement, and has become one of the research hotspots in the field of medical image processing. This paper discusses the latest research progress and application of deep learning in medical image reconstruction and enhancement. Firstly, the importance of medical image reconstruction and enhancement and the limitations of traditional methods are introduced. Then, a detailed discussion was conducted on the application of deep learning models, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Autoencoders, in medical image processing. Specifically, an analysis and comparison were conducted on the image reconstruction ability of CNN models, the image enhancement effect of GAN models, and the image denoising and reconstruction of Autoencoder models. Then, the advantages and challenges of deep learning model in medical image processing are discussed, and the future development direction is discussed. Finally, the research results of this paper are summarized and the prospect of future research is put forward. The research in this paper provides some enlightenment and reference for researchers and practitioners in the field of medical image processing, which is helpful to promote the continuous innovation and progress of medical image processing technology.

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Section: Application Of Deep Learning In Medical Image Reconstruction...mentioning

confidence: 99%