Spectral Camera based on Ghost Imaging via Sparsity Constraints

Abstract

The image information acquisition ability of a conventional camera is usually much lower than the Shannon Limit since it does not make use of the correlation between pixels of image data. Applying a random phase modulator to code the spectral images and combining with compressive sensing (CS) theory, a spectral camera based on true thermal light ghost imaging via sparsity constraints (GISC spectral camera) is proposed and demonstrated experimentally. GISC spectral camera can acquire the information at a rate significantly below the Nyquist rate, and the resolution of the cells in the three-dimensional (3D) spectral images data-cube can be achieved with a two-dimensional (2D) detector in a single exposure. For the first time, GISC spectral camera opens the way of approaching the Shannon Limit determined by Information Theory in optical imaging instruments.

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Cited by in corpus (5)

• Ghost imaging LiDAR via sparsity constraints using push-broom scanning
• Ghost imaging based on Y-net: a dynamic coding and conjugate-decoding approach
• Optimization of light fields in ghost imaging using dictionary learning
• Single-pixel diffuser camera
• Lensless Wiener-Khinchin telescope based on high-order spatial autocorrelation of thermal light