Fourier ptychographic microscopy (FPM) is a recently developed technique stitching low-resolution images in Fourier domain to realize wide-field high-resolution imaging. However, the time-consuming process of image acquisition greatly narrows its applications in dynamic imaging. We report a wavelength multiplexing strategy to speed up the acquisition process of FPM several folds. A proof-of-concept system is built to verify its feasibility. Distinguished from many current multiplexing methods in Fourier domain, we explore the potential of high-speed FPM in spectral domain. Compatible with most existing FPM methods, our strategy provides an approach to high-speed gigapixel microscopy. Several experimental results are also presented to validate the strategy.
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.
Non-uniform image blur caused by camera shake or lens aberration is a common degradation in practical capture. Different from the uniform blur, non-uniform one is hard to deal with for its extremely high computation complexity as the blur model computation cannot be accelerated by Fast Fourier Transform (FFT). We propose to compute the most computational consuming operation, i.e. blur model calculation, by an optical computing system to realize fast and accurate non-uniform image deblur. A prototype system composed by a projector-camera system as well as a high dimensional motion platform (for motion blur) or original camera lens (for optics aberrations) is implemented. Our method is applied on a series of experiments, either on synthetic or real captured images, to verify its effectiveness and efficient.
The hyper-spectrum data exhibits the structure, materials, and semantic meaning of a nature scene and its fast acquisition is of great importance due to its potential for parse these properties of dynamic scenes. Targeting for high speed hyperspectrum imaging of a nature scene, this paper proposes to capture the coded hyper-spectrum reflectance of a nature scene using low cost hardware and reconstruct the latent data using a corresponding decoding algorithm. Except for a wide spectrum light source, the imaging system includes mainly a commercially available projector color wheel and a high speed camera, which work at their own constant periods and are self-synchronized by our algorithm. The introduced light source and color wheel cost less than 50 dollars and makes the proposed approach widely available. The results on the data captured by our prototype system show that, the proposed approach can reconstruct the high precision hyper-spectrum data at real time.
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.