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Synthesizing hyperspectral images (HSI) from an ordinary camera has been accomplished recently. However, such computation models require detailed properties of the target camera, which can only be measured in a professional lab. This prerequisite prevents the synthesizing model from being installed on arbitrary cameras for end-users. This study offers a calibration-free method for transforming any camera into an HSI camera. Our solution requires no controllable light sources and spectrometers. Any consumer installing the program should produce high-quality HSI without the assistance of optical laboratories. Our approach facilitates a cycle-generative adversarial network (cycle-GAN) and sparse assimilation method to render the illumination-dependent spectral response function (SRF) of the underlying camera at the first part of the setup stage. The current illuminating function (CIF) must be identified for each image and decoupled from the underlying model. The HSI model is then integrated with the static SRF and dynamic CIF in the second part of the stage. The estimated SRFs and CIFs have been double-checked with the results by the standard laboratory method. The reconstructed HSIs have errors under 3% in the root mean square.

Synthesizing hyperspectral images (HSI) from an ordinary camera has been accomplished recently. However, such computation models require detailed properties of the target camera, which can only be measured in a professional lab. This prerequisite prevents the synthesizing model from being installed on arbitrary cameras for end-users. This study offers a calibration-free method for transforming any camera into an HSI camera. Our solution requires no controllable light sources and spectrometers. Any consumer installing the program should produce high-quality HSI without the assistance of optical laboratories. Our approach facilitates a cycle-generative adversarial network (cycle-GAN) and sparse assimilation method to render the illumination-dependent spectral response function (SRF) of the underlying camera at the first part of the setup stage. The current illuminating function (CIF) must be identified for each image and decoupled from the underlying model. The HSI model is then integrated with the static SRF and dynamic CIF in the second part of the stage. The estimated SRFs and CIFs have been double-checked with the results by the standard laboratory method. The reconstructed HSIs have errors under 3% in the root mean square. [ABSTRACT FROM AUTHOR]