Compressive Spectral Imaging with Diffractive Lenses
Oguzhan Fatih Kar,Figen S. Oktem +1 more
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TLDR
In this article, a coded aperture is used to spatially modulate the optical field from the scene and a diffractive lens such as a photon-sieve is used for dispersion.Abstract:
Compressive spectral imaging enables to reconstruct the entire three-dimensional (3D) spectral cube from a few multiplexed images. Here, we develop a novel compressive spectral imaging technique using diffractive lenses. Our technique uses a coded aperture to spatially modulate the optical field from the scene and a diffractive lens such as a photon-sieve for dispersion. The coded field is passed through the diffractive lens and then measured at a few planes using a monochrome detector. The 3D spectral cube is then reconstructed from these highly compressed measurements through sparse recovery. A fast sparse recovery method is developed to solve this large-scale inverse problem. The imaging performance is illustrated at visible regime for various scenarios with different compression ratios through numerical simulations. The results demonstrate that promising reconstruction performance can be achieved with as little as two measurements. This opens up new possibilities for high resolution spectral imaging with low-cost and simple designs.read more
Citations
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Spectral imaging with deep learning
TL;DR: In this article , a review of state-of-the-art deep learning-empowered computational spectral imaging methods is presented, which is further divided into amplitude-coded, phase-coded and wavelength-coded methods, based on different light properties used for encoding.
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Spectral imaging with deep learning
TL;DR: In this article , a review of state-of-the-art deep learning-empowered computational spectral imaging methods is presented, which is further divided into amplitude-coded, phase-coded and wavelength-coded methods, based on different light properties used for encoding.
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Dual-camera snapshot spectral imaging with a pupil-domain optical diffuser and compressed sensing algorithms
Jonathan Hauser,Michael A. Golub,Amir Averbuch,Menachem Nathan,Valery A. Zheludev,Michael Kagan +5 more
TL;DR: Results of optical experiments confirm that the combined data from the two cameras relax the complexity of the underdetermined reconstruction problem and improve the reconstructed image quality obtained using compressed sensing-based algorithms.
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High-resolution Multi-spectral Imaging with Diffractive Lenses and Learned Reconstruction.
TL;DR: A novel multi-spectral imaging modality that enables higher spatial and spectral resolutions and the capability of resolving close-by spectral sources that would not otherwise be possible with the existing techniques is developed.
Proceedings ArticleDOI
Diffractive optical imaging spectrometer with reference channel
Haibo Zhao,Yanli Liu,Xiaojie Yu,Jing Xu,Yechao Wang,Lisha Zhang,Xiaoming Zhong,Fang Xue,Quansen Sun +8 more
TL;DR: In this article, an efficient system of diffractive spectral imaging is discussed, which includes a reference channel, based on the conventional single-channel system, a grayscale camera or a color camera is added for imaging.
References
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