Abstract
Utilizing multiple spectral bands provides more information compared to the limited number of channels present in RGB images. However, hyperspectral imaging equipment is expensive. It also poses a hindrance in terms of availability and ease of operation. This work aims to explore the potential Generative Adversarial Networks (GANs) have to generate 31-band hyperspectral images from RGB images. In this work, we employ the idea of neural oscillations to train GANs. Various experiments were conducted on different generative and discriminative models, utilizing different training techniques. The results show that GANs and variable training ratios are a promising approach for hyperspectral image generation.
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Sinha, H., Kumar, S., Chaudhury, S. (2021). A Variational Training Perspective to GANs for Hyperspectral Image Generation. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1392 . Springer, Singapore. https://doi.org/10.1007/978-981-16-2709-5_32
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DOI: https://doi.org/10.1007/978-981-16-2709-5_32
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