Single exposure passive three-dimensional information reconstruction based on an ordinary imaging system

Existing three-dimensional(3D) imaging technologies have issues such as requiring active illumination, multiple exposures, or coding modulation. We propose a passive single 3D imaging method based on an ordinary imaging system.Using the point spread function of the imaging system to realize the non-coding measurement on the target, the full-focus images and depth information of the 3D target can be extracted from a single two-dimensional(2D) image through the compressed sensing algorithm. Simulation and experiments show that this approach can complete passive 3D imaging based on an ordinary imaging system without any coding operations. This method can achieve millimeter-level vertical resolution under single exposure conditions and has the potential for real-time dynamic 3D imaging. It improves the efficiency of 3D information detection, reduces the complexity of the imaging system, and may be of considerable value to the field of computer vision and other related applications.

Computational Spectral Imaging Based on Compressive Sensing

Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing （CS）. The spectral and spatial infor- mation is simultaneously obtained using a fiber spectrometer and the spatial light modulation without mechanical scanning. The method allows high-speed, stable, and sub sampling acquisition of spectral data from specimens. The relationship between sampling rate and image quality is discussed and two CS algorithms are compared.

Compressive imaging based on multi-scale modulation and reconstruction in spatial frequency domain

Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency domain. Theoretical analysis and simulation show the relation between the measurement matrix resolution and compressive sensing(CS)imaging quality. The matrix design is improved to provide multi-scale modulations, followed by individual reconstruction of images of different spatial frequencies. Compared with traditional single-scale CS imaging, the multi-scale method provides high quality imaging in both high and low frequencies, and effectively decreases the overall reconstruction error.Experimental results confirm the feasibility of this technique, especially at low sampling rate. The method may thus be helpful in promoting the implementation of compressive imaging in real applications.

Pseudo complementary measurement for traditional single-pixel cameras

A traditional single-pixel camera needs a large number of measurements to reconstruct the object with compressive sensing computation.Compared with the 1/0 matrices in classical measurement,the 1/-1 matrices in the complementary measurement has better property for reconstruction computation and returns better reconstruction results.However,each row of the 1/-1 matrices needs two measurements with the traditional single-pixel camera which results into double measurements compared with the 1/0 matrices.In this paper,we consider the pseudo complementary measurement which only takes the same amount of measurements with the row number of some properly designed 1/0 matrix to compute the total luminous flux of the objective and derives the measurement data of the corresponding 1/-1 matrix in a mathematical way.The numerical simulation and experimental result show that the pseudo complementary measurement is an efficient tool for the traditional single-pixel camera imaging under low measurement rate,which can combine the advantages of the classical and complementary measurements and significantly improve the peak signal-to-noise ratio.

Sub-Rayleigh imaging via undersampling scanning based on sparsity constraints

We demonstrate that, by undersampling scanning object with a reconstruction algorithm related to compressed sensing, an image with the resolution exceeding the finest resolution defined by the numerical aperture of the system can be obtained. Experimental results show that the measurements needed to achieve sub-Rayleigh resolution enhancement can be less than 10% of the pixels of the object. This method offers a general approach applicable to point-by-point illumination super-resolution techniques.