HEXA-GAN: Skin lesion image inpainting via hexagonal sampling based generative adversarial network

Bansal, Nidhi; Sridhar, S.

doi:10.1016/j.bspc.2023.105603

Cited by 6 publications

(1 citation statement)

References 36 publications

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“…Recently, the research related to GANs has attracted much attention; typical applications include image de-raining [30][31][32], image inpainting [25,33,34], image reconstruction [35][36][37], and image synthesis [38][39][40]. Image de-raining is a computer vision task that involves removing or reducing the effects of rain from images.…”

Section: Introductionmentioning

confidence: 99%

Color Face Image Generation with Improved Generative Adversarial Networks

Chang,

Chung,

Chai

et al. 2024

Electronics

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This paper focuses on the development of an improved Generative Adversarial Network (GAN) specifically designed for generating color portraits from sketches. The construction of the system involves using a GPU (Graphics Processing Unit) computing host as the primary unit for model training. The tasks that require high-performance calculations are handed over to the GPU host, while the user host only needs to perform simple image processing and use the model trained by the GPU host to generate images. This arrangement reduces the computer specification requirements for the user. This paper will conduct a comparative analysis of various types of generative networks which will serve as a reference point for the development of the proposed Generative Adversarial Network. The application part of the paper focuses on the practical implementation and utilization of the developed Generative Adversarial Network for the generation of multi-skin tone portraits. By constructing a face dataset specifically designed to incorporate information about ethnicity and skin color, this approach can overcome a limitation associated with traditional generation networks, which typically generate only a single skin color.

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