<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study proposes an image enhancement method combining conditional entropy and generative adversarial network, aiming to improve the image quality while avoiding overfitting through the negative training of dependent generative adversarial network and introducing dependent entropy distance loss. Through NIQMC, NIQE and BTMQI evaluation indexes, this paper evaluates the effects of different parameter combinations and image chunk sizes on the enhancement results. It utilizes information entropy as an evaluation index to measure the impact of conditional entropy distance loss. The effectiveness of adversarial learning and conditional entropy in image enhancement is verified by comparing the experimental results. The experiments show that the system can achieve the best image quality of SSIM=0.9852, PSNR=27.58, and SNROI=21.34 with the parameters S=50 and R=4.0%, indicating that the method can effectively retain the detailed information and realism of the Image while enhancing the clarity of the Image, demonstrating a significant performance advantage.</p>
</abstract>

This study proposes an image enhancement method combining conditional entropy and generative adversarial network, aiming to improve the image quality while avoiding overfitting through the negative training of dependent generative adversarial network and introducing dependent entropy distance loss. Through NIQMC, NIQE and BTMQI evaluation indexes, this paper evaluates the effects of different parameter combinations and image chunk sizes on the enhancement results. It utilizes information entropy as an evaluation index to measure the impact of conditional entropy distance loss. The effectiveness of adversarial learning and conditional entropy in image enhancement is verified by comparing the experimental results. The experiments show that the system can achieve the best image quality of SSIM=0.9852, PSNR=27.58, and SNROI=21.34 with the parameters S=50 and R=4.0%, indicating that the method can effectively retain the detailed information and realism of the Image while enhancing the clarity of the Image, demonstrating a significant performance advantage.

Research on Medical Image Enhancement Method Based on Conditional Entropy Generative Adversarial Networks

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study proposes an image enhancement method combining conditional entropy and generative adversarial network, aiming to improve the image quality while...

Research on Medical Image Enhancement Method Based on Conditional Entropy Generative Adversarial Networks

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 02 gen 2024

Accettato: 07 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0337

Parole chiave
Conditional entropy, Generative adversarial network, Image chunk size, Medical image enhancement

© 2024 Hui Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on Medical Image Enhancement Method Based on Conditional Entropy Generative Adversarial Networks

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 02 gen 2024

Accettato: 07 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0337

Parole chiaveConditional entropy, Generative adversarial network, Image chunk size, Medical image enhancement

© 2024 Hui Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Conditional entropy, Generative adversarial network, Image chunk size, Medical image enhancement