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Journal of Imaging, Volume 11, Issue 8
August 2025 - 40 articles
Cover Story: From decoding plant root networks to simulating the inner structure of rocks, our research explores how generative AI is transforming scientific imaging. We put the most advanced architectures, ranging from VAEs to GANs and diffusion models, to the test on microCT scans, composite fibers, and high-resolution biological images. GANs, led by StyleGAN, produced strikingly detailed and coherent images, while diffusion models like DALL-E 2 delivered remarkable realism but sometimes sacrificed scientific precision. Our findings reveal why common image quality scores fall short in science, and why expert review is essential. By tackling challenges in interpretability, cost, and verification, we outline how generative AI could soon power breakthroughs in data augmentation, simulation, and even scientific discovery itself. View this paper
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J. Imaging - ISSN 2313-433X