Title
Multispectral Image Reconstruction From Color Images Using Enhanced Variational Autoencoder And Generative Adversarial Network
Abstract
Since multispectral images (MSIs) have much more sufficient spectral information than RGB images (RGBs), reconstructing MS images from RGB images is a severely underconstrained problem. We have to generate colossally different information between the two scopes. Almost all previous approaches are based on static and dependent neural networks, which fail to explain how to supplement the massive lost information. This paper presents a low-cost and high-efficiency approach, "VAE-GAN", based on stochastic neural networks to directly reconstruct high-quality MSIs from RGBs. Our approach combines the advantages of the Generative Adversarial Network (GAN) and the Variational Autoencoder (VAE). The VAE undertakes the generation of the lost variational MS distributions by reparameterizing the latent space vector with sampling from Gaussian distribution. The GAN is responsible for regulating the generator to produce MSI-like images. In this way, our approach can create huge missed information and make the outputs look real, which also solves the previous problem. Moreover, we use several qualitative and quantitative methods to evaluate our approach and obtain excellent results. In particular, with much less training data than the previous approaches, we obtained comparable results on the CAVE dataset and surpassed state-of-the-art results on the ICVL dataset.
Year
DOI
Venue
2021
10.1109/ACCESS.2020.3047074
IEEE ACCESS
Keywords
DocType
Volume
Generative adversarial network (GAN), variational autoencoder (VAE), VAE-GAN, normal distribution, stochastic neural network, multispectral image, RGB image, image processing, color vision, spectral reconstruction
Journal
9
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Xu Liu100.68
abdelouahed gherbi215124.82
Zhenzhou Wei300.34
Wubin Li400.34
Mohamed Cheriet52047238.58