Compressive spectral imaging with diffractive lenses

Abstract

Compressive spectral imaging enables to reconstruct the entire 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 both dispersion and focusing. Measurement diversity is achieved by changing the focusing behavior of the diffractive lens. The 3D spectral cube is then reconstructed from highly compressed measurements taken with a monochrome detector. A fast sparse recovery method is developed to solve this large-scale inverse problem. The performance is illustrated at visible regime for various scenarios with different compression ratios through simulations. The results demonstrate that promising reconstruction performance can be achieved at high compression levels. This opens up new possibilities for high resolution spectral imaging with low-cost and simpler designs.

Computational Spectral Imaging With Diffractive Lenses And Spectral Filter Arrays

“…For measurement diversity, multiple measurements can also be taken by changing the diffractive lens design such as with a programmable spatial light modulator. For comparison purpose, the system used in the recent coded aperture spectral imaging technique with diffractive lenses (CSID) [8] is also illustrated in Fig. 1, which additionally requires a coded aperture and an imaging lens.…”

“…Here x s [m, n] represents the sth spectral image (with central wavelength λ s ), which is convolved with the point-spread function (PSF) of the diffractive lens at this wavelength. This PSF is denoted by h s,k [m, n] (with k representing the measurement index), whose closed-form expression can be found elsewhere [8,20]. The measurement y k [m, n] is composed of the convolved (blurred) versions of the spectral images which are also multiplied with the response d s [m, n] of the multispectral sensor at the respective detector pixel.…”

“…In order to solve this optimization problem, we convert our constrained problem into an unconstrained problem by adding the constraint into the objective function as a penalty. Then an alternating direction method of multipliers (ADMM) method is developed by following the similar steps to [8]. For this, variable splitting is first applied to the unconstrained problem: 1) ,z (2) ||Φz (1)…”

“…Most of these techniques focus on compressive or snapshot imaging, enabling to reconstruct the entire spectral cube from a few multiplexed measurements. For this purpose, various system architectures have been proposed using different dispersive elements (exhibiting wavelength-dependent behavior) like prisms/gratings [5,6], diffusers [7], and diffractive lenses [8][9][10][11][12][13].…”

A High Optical Throughput Spectral Imaging Technique Using Broadband Filters

“…To solve the problems of traditional spectral imaging, new types of computational spectral imaging technologies such as computational tomography [22], the Hadamard transform [23] and compression coding [24][25][26] have been gradually developed. Computational tomography uses advanced gratings to produce overlapping projections of spectral cubes on 2D sensors.…”