Lensless in-line holography is a simple, portable, and cost-effective method of imaging especially for the biomedical microscopy applications. We propose a multiplicative gradient descent optimization based method to obtain multi-depth imaging from a single hologram acquired in this imaging system. We further extend the method to achieve phase imaging from a single hologram. Negative-log-likelihood functional with the assumption of poisson noise has been used as the cost function to be minimized. The ill-posed nature of the problem is handled by the sparse regularization and the upper-bound constraint. The gradient descent optimization requires calculation of the partial derivative of the cost function with respect to a given estimate of the object. A method of obtaining this quantity for holography in both the cases of real object and complex object has been shown. The reconstruction method has been validated using extensive simulation and experimental studies. The comparison with the previously established iterative shrinkage/thresholding algorithm based compressive holography shows that the proposed method has the following advantages: significantly faster convergence rate, better reconstructed image quality and the ability to perform phase imaging.
Fresnel incoherent correlation holography (FINCH) is a self-interference based super-resolution three-dimensional imaging technique. FINCH in inline configuration requires an active phase modulator and at least three camera shots to reconstruct objects without the twin image and bias terms. In this study, FINCH is realized using a randomly multiplexed bifocal binary Fresnel zone lenses fabricated using electron beam lithography. A modified hologram reconstruction mechanism is presented which introduces the single shot capability in FINCH. A point spread hologram library was recorded using a po…
University of California, Berkeley（加州大学伯克利分校）；Orange Coast College（奥兰治海岸学院）
Compressive lensless imagers enable novel applications in an extremely compact device, requiring only a phase or amplitude mask placed close to the sensor. They have been demonstrated for 2D and 3D microscopy, single-shot video, and single-shot hyperspectral imaging; in each of these cases, a compressive-sensing-based inverse problem is solved in order to recover a 3D data-cube from a 2D measurement. Typically, this is accomplished using convex optimization and hand-picked priors. Alternatively, deep learning-based reconstruction methods offer the promise of better priors, but require many tho…
In recent years nanoscale coherent imaging has emerged as an indispensable imaging modality allowing to surpass the resolution limit given by classical imaging optics. At the same time, attosecond science has experienced enormous progress and has revealed the ultrafast dynamics in atoms, molecules, and complex materials. Combining attosecond temporal resolution of pump-probe experiments with nanometer spatial resolution would allow studying ultrafast dynamics on the smallest spatio-temporal scales but has not been demonstrated yet. Unfortunately, the large bandwidth of attosecond pulses usuall…
We demonstrate single-shot compressive three-dimensional (3D) $(x, y, z)$ imaging based on interference coding. The depth dimension of the object is encoded into the interferometric spectra of the light field, resulting a $(x, y, λ)$ datacube which is subsequently measured by a single-shot spectrometer. By implementing a compression ratio up to $400$, we are able to reconstruct $1G$ voxels from a 2D measurement. Both an optimization based compressive sensing algorithm and a deep learning network are developed for 3D reconstruction from a single 2D coded measurement. Due to the fast acquisition…
Bell Labs（贝尔实验室）；College of Optical Sciences, University of Arizona（亚利桑那大学光学科学学院）；Northwestern University（西北大学）
Capturing high-dimensional (HD) data is a long-term challenge in signal processing and related fields. Snapshot compressive imaging (SCI) uses a two-dimensional (2D) detector to capture HD ($\ge3$D) data in a {\em snapshot} measurement. Via novel optical designs, the 2D detector samples the HD data in a {\em compressive} manner; following this, algorithms are employed to reconstruct the desired HD data-cube. SCI has been used in hyperspectral imaging, video, holography, tomography, focal depth imaging, polarization imaging, microscopy, \etc.~Though the hardware has been investigated for more t…
Snapshot compressive imaging (SCI) systems aim to capture high-dimensional ($\ge3$D) images in a single shot using 2D detectors. SCI devices include two main parts: a hardware encoder and a software decoder. The hardware encoder typically consists of an (optical) imaging system designed to capture {compressed measurements}. The software decoder on the other hand refers to a reconstruction algorithm that retrieves the desired high-dimensional signal from those measurements. In this paper, using deep unfolding ideas, we propose an SCI recovery algorithm, namely GAP-net, which unfolds the general…
Complex object wave recovery from single-shot interference pattern is an important practical problem in interferometry and digital holography. The most popular single-shot interferogram analysis method involves Fourier filtering of cross-term but this method suffers from poor resolution. For obtaining full pixel resolution, it is necessary to model the object wave recovery as an optimization problem. The optimization approach typically involves minimizing a cost function consisting of a data consistency term and one or more constraint terms. Despite its potential performance advantages, this m…
Latent Diffusion Prior Enhanced Deep Unfolding for Snapshot Spectral Compressive Imaging
Zhejiang University（浙江大学）；Westlake University（西湖大学）；School of Engineering, Westlake University（西湖大学工程学院）；Xidian University（西安电子科技大学）；School of Cyber Engineering, Xidian University（西安电子科技大学网络与工程学院）；Beijing Institute of Technology（北京理工大学）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）
Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Tsinghua University（清华大学）；Peking University（北京大学）
Hyperspectral imaging plays a pivotal role in a wide range of applications, like remote sensing, medicine, and cytology. By acquiring 3D hyperspectral images (HSIs) via 2D sensors, the coded aperture snapshot spectral imaging (CASSI) has achieved great success due to its hardware-friendly implementation and fast imaging speed. However, for some less spectrally sparse scenes, single snapshot and unreasonable coded aperture design tend to make HSI recovery more ill-posed and yield poor spatial and spectral fidelity. In this paper, we propose a novel Progressive Content-Aware CASSI framework, dub…
Zero-Shot Solving of Imaging Inverse Problems via Noise-Refined Likelihood Guided Diffusion Models
School of Mathematics and Statistics（数学与统计学学院）；Nanjing University of Science and Technology（南京理工大学）；School of Computer Science and Engineering（计算机科学与工程学院）
A generic computational imaging setup is considered which assumes sequential illumination of a semi-transparent object by an arbitrary set of structured illumination patterns. For each incident illumination pattern, all transmitted light is collected by a photon-counting bucket (single-pixel) detector. The transmission coefficients measured in this way are then used to reconstruct the spatial distribution of the object's projected transmission. It is demonstrated that the squared spatial resolution of such a setup is usually equal to the ratio of the image area to the number of linearly indepe…
