Open Access

ARTICLE

Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

Hong’an Li¹, Min Zhang^1,*, Dufeng Chen², Jing Zhang¹, Meng Yang³, Zhanli Li¹

1 College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, 710054, China
2 Beijing Geotechnical and Investigation Engineering Insititute, Beijing, 100080, China
3 Xi’an Institute of Applied Optics, Xi’an, 710065, China

* Corresponding Author: Min Zhang. Email:

(This article belongs to the Special Issue: Advances in Edge Intelligence for Internet of Things)

Computer Modeling in Engineering & Sciences 2023, 135(1), 779-794. https://doi.org/10.32604/cmes.2022.022369

Received 07 March 2022; Accepted 27 May 2022; Issue published 29 September 2022

Abstract

Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis. To overcome the limitations of the color rendering method based on deep learning, such as poor model stability, poor rendering quality, fuzzy boundaries and crossed color boundaries, we propose a novel hinge-cross-entropy generative adversarial network (HCEGAN). The self-attention mechanism was added and improved to focus on the important information of the image. And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models. In this study, we implement the HCEGAN model for image color rendering based on DIV2 K and COCO datasets, and evaluate the results using SSIM and PSNR. The experimental results show that the proposed HCEGAN automatically re-renders images, significantly improves the quality of color rendering and greatly improves the stability of prior GAN models.

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