Existing multispectral imagers mostly use 2D array sensors to separately measure 2D data slices in a 3D spatialspectral data cube. They suffer from low photon efficiency, limited spectral range, and high cost. To address these issues, we propose to conduct multispectral imaging using a photodiode, to take full advantage of its high sensitivity, wide spectral range, low cost, and small size. Specifically, utilizing the photodiode’s fast response, a scene’s 3D spatial-spectral information is sinusoidally multiplexed into a dense 1D measurement sequence, and then demultiplexed computationally under the single-pixel imaging scheme. A proof-of-concept setup is built to capture multispectral data of 256 pixels × 256 pixels × 10 wavelength bands ranging from 450 nm to 650 nm. The imaging scheme holds great potentials for various biological applications such as fluorescence microscopy and endoscopy.
Conventional multispectral imaging methods detect photons of a 3D hyperspectral data cube separately either in the spatial or spectral dimension using array detectors, and are thus photon inefficient and spectrum range limited. Besides, they are usually bulky and highly expensive. To address these issues, this paper presents single-pixel multispectral imaging techniques, which are of high sensitivity, wide spectrum range, low cost and light weight. Two mechanisms are proposed, and experimental validation are also reported.
Optical coherence tomography (OCT) is an important interferometric diagnostic technique, which provides cross-sectional views of biological tissues’ subsurface microstructures. However, the imaging quality of high-speed OCT is limited by the large speckle noise. To address this problem, we propose a multiframe algorithmic method to denoise OCT volume. Mathematically, we build an optimization model which forces the temporally registered frames to be low-rank and the gradient in each frame to be sparse, under the constraints from logarithmic image formation and nonuniform noise variance. In addition, a convex optimization algorithm based on the augmented Lagrangian method is derived to solve the above model. The results reveal that our approach outperforms the other methods in terms of both speckle noise suppression and crucial detail preservation.
Capturing four dimensional light field data sequentially using a coded aperture camera is an effective approach but
suffers from low signal noise ratio. Although multiplexing can help raise the acquisition quality, noise is still a big issue
especially for fast acquisition. To address this problem, this paper proposes a noise robust light field reconstruction
method. Firstly, scene dependent noise model is studied and incorporated into the light field reconstruction framework.
Then, we derive an optimization algorithm for the final reconstruction. We build a prototype by hacking an off-the-shelf
camera for data capturing and prove the concept. The effectiveness of this method is validated with experiments on the
real captured data.