Lensless computational imaging with a hybrid framework of holographic propagation and deep learning.

Lensless imaging has attracted attention as it avoids the bulky optical lens. Lensless holographic imaging is a type of a lensless imaging technique. Recently, deep learning has also shown tremendous potential in lensless holographic imaging. A labeled complex field including real and imaginary components of the samples is usually used as a training dataset. However, obtaining such a holographic dataset is challenging. In this Letter, we propose a lensless computational imaging technique with a hybrid framework of holographic propagation and deep learning. The proposed framework takes recorded holograms as input instead of complex fields, and compares the input and regenerated holograms. Compared to previous supervised learning schemes with a labeled complex field, our method does not require this supervision. Furthermore, we use the generative adversarial network to constrain the proposed framework and tackle the trivial solution. We demonstrate high-quality reconstruction with the proposed framework compared to previous deep learning methods.

Fast non-line-of-sight imaging with high-resolution and wide field of view using synthetic wavelength holography.

The presence of a scattering medium in the imaging path between an object and an observer is known to severely limit the visual acuity of the imaging system. We present an approach to circumvent the deleterious effects of scattering, by exploiting spectral correlations in scattered wavefronts. Our Synthetic Wavelength Holography (SWH) method is able to recover a holographic representation of hidden targets with sub-mm resolution over a nearly hemispheric angular field of view. The complete object field is recorded within 46 ms, by monitoring the scattered light return in a probe area smaller than 6 cm × 6 cm. This unique combination of attributes opens up a plethora of new Non-Line-of-Sight imaging applications ranging from medical imaging and forensics, to early-warning navigation systems and reconnaissance. Adapting the findings of this work to other wave phenomena will help unlock a wider gamut of applications beyond those envisioned in this paper.

Exploiting Wavelength Diversity for High Resolution Time-of-Flight 3D Imaging.

The poor lateral and depth resolution of state-of-the-art 3D sensors based on the time-of-flight (ToF) principle has limited widespread adoption to a few niche applications. In this work, we introduce a novel sensor concept that provides ToF-based 3D measurements of real world objects and surfaces with depth precision up to 35 µm and point cloud densities commensurate with the native sensor resolution of standard CMOS/CCD detectors (up to several megapixels). Such capabilities are realized by combining the best attributes of continuous wave ToF sensing, multi-wavelength interferometry, and heterodyne interferometry into a single approach. We describe multiple embodiments of the approach, each featuring a different sensing modality and associated tradeoffs.

Compressive ghost imaging through scattering media with deep learning.

Imaging through scattering media is challenging since the signal to noise ratio (SNR) of the reflection can be heavily reduced by scatterers. Single-pixel detectors (SPD) with high sensitivities offer compelling advantages for sensing such weak signals. In this paper, we focus on the use of ghost imaging to resolve 2D spatial information using just an SPD. We prototype a polarimetric ghost imaging system that suppresses backscattering from volumetric media and leverages deep learning for fast reconstructions. In this work, we implement ghost imaging by projecting Hadamard patterns that are optimized for imaging through scattering media. We demonstrate good quality reconstructions in highly scattering conditions using a 1.6% sampling rate.

Case-based reasoning based on grey-relational theory for the optimization of boiler combustion systems.

Boiler combustion optimization is an important method to improve the flexibility of thermal power units and ensures the stability of unit operation. However, time-variability of boiler combustion systems and time-consuming optimization methods pose great challenges for the use of boiler combustion optimization techniques because many optimization methods cannot be used online in practical engineering due to time constraints. In this paper, we propose a case-based reasoning optimization method based on grey-relational theory (GR-CBR) for online optimization of a boiler combustion system. After the introduction of the proposed algorithm, we discuss the potential of applying the proposed GR-CBR optimization method to a boiler combustion system; a case study of an existing fossil fuel power plant is conducted to demonstrate the feasibility of the proposed method. A least-squares support vector machine (LS-SVM) model of the boiler combustion process is established by using the real-time operation data of a 350-MW coal-based power plant. Based on the model, a non-linear global optimization algorithm is proposed to obtain the optimal case base and real-time data mining and online optimization are used to achieve efficient and stable boiler combustion optimization. The results of combining offline optimization with online querying show that this approach is suitable for online real-time combustion optimization, and provides support for power plant operators for optimization and condition monitoring to improve boiler efficiency, reduce NOx emissions, and ensure stable and efficient operation of the power system.

CS-ToF: High-resolution compressive time-of-flight imaging.

Three-dimensional imaging using Time-of-flight (ToF) sensors is rapidly gaining widespread adoption in many applications due to their cost effectiveness, simplicity, and compact size. However, the current generation of ToF cameras suffers from low spatial resolution due to physical fabrication limitations. In this paper, we propose CS-ToF, an imaging architecture to achieve high spatial resolution ToF imaging via optical multiplexing and compressive sensing. Our approach is based on the observation that, while depth is non-linearly related to ToF pixel measurements, a phasor representation of captured images results in a linear image formation model. We utilize this property to develop a CS-based technique that is used to recover high resolution 3D images. Based on the proposed architecture, we developed a prototype 1-megapixel compressive ToF camera that achieves as much as 4× improvement in spatial resolution and 3× improvement for natural scenes. We believe that our proposed CS-ToF architecture provides a simple and low-cost solution to improve the spatial resolution of ToF and related sensors.

High-depth-resolution range imaging with multiple-wavelength superheterodyne interferometry using 1550-nm lasers.

Lasers and laser diodes are widely used as illumination sources for optical imaging techniques. Time-of-flight (ToF) cameras with laser diodes and range imaging based on optical interferometry systems using lasers are among these techniques, with various applications in fields such as metrology and machine vision. ToF cameras can have imaging ranges of several meters, but offer only centimeter-level depth resolution. On the other hand, range imaging based on optical interferometry has depth resolution on the micrometer and even nanometer scale, but offers very limited (sub-millimeter) imaging ranges. In this paper, we propose a range imaging system based on multi-wavelength superheterodyne interferometry to simultaneously provide sub-millimeter depth resolution and an imaging range of tens to hundreds of millimeters. The proposed setup uses two tunable III-V semiconductor lasers and offers leverage between imaging range and resolution. The system is composed entirely of fiber connections except the scanning head, which enables it to be made into a portable device. We believe our proposed system has the potential to tremendously benefit many fields, such as metrology and computer vision.

[Microdecompression for intraforaminal lumbar disc herniations].

OBJECTIVE: To summarize clinical results of the microdecompression for the treatment of intraforaminal lumbar disc herniations. METHODS: From September 2005 to May 2013,16 patients( 12 males, 4 females)with intraforaminal lumbar disc herniations underwent microdecompression, ranging in age from 32 to 56 years old with a mean of 38.6 years old. The lumbar disc herniations were located at L(3,4). in one patient, L(4,5) in 10 cases and L5S1 in 5 cases. RESULTS: All the patients were followed up, and the duration ranged from 20 to 48 months, with a mean period of 36 months. According to Macnab evaluation, 12 cases got an excellent result, 4 good. No apparent complications related to the technique occurred. Satisfactory clinical results were obtained in this series. CONCLUSION: Microdecompression may be particularly useful in the treatment of intraforaminal lumbar disc herniations. The microdecompression procedures are more likely to be well tolerated by older patients.

Label-free evaluation of angiogenic sprouting in microengineered devices using ultrahigh-resolution optical coherence microscopy.

Understanding the mechanism of angiogenesis could help to decipher wound healing and embryonic development and to develop better treatment for diseases such as cancer. Microengineered devices were developed to reveal the mechanisms of angiogenesis, but monitoring the angiogenic process nondestructively in these devices is a challenge. In this study, we utilized a label-free imaging technique, ultrahigh-resolution optical coherence microscopy (OCM), to evaluate angiogenic sprouting in a microengineered device. The OCM system was capable of providing ∼1.5-µm axial resolution and ∼2.3-µm transverse resolution. Three-dimensional (3-D) distribution of the sprouting vessels in the microengineered device was imaged over 0.6×0.6×0.5 mm3, and details such as vessel lumens and branching points were clearly visualized. An algorithm based on stretching open active contours was developed for tracking and segmenting the sprouting vessels in 3-D-OCM images. The lengths for the first-, second-, and third-order vessels were measured as 127.8±48.8 µm (n=8), 67.3±25.9 µm (n=9), and 62.5±34.7 µm (n=10), respectively. The outer diameters for the first-, second-, and third-order vessels were 13.2±1.0, 8.0±2.1, and 4.4±0.8 µm, respectively. These results demonstrate OCM as a promising tool for nondestructive and label-free evaluation of angiogenic sprouting in microengineered devices.

Nondestructive evaluation of progressive neuronal changes in organotypic rat hippocampal slice cultures using ultrahigh-resolution optical coherence microscopy.

Three-dimensional tissue cultures have been used as effective models for studying different diseases, including epilepsy. High-throughput, nondestructive techniques are essential for rapid assessment of disease-related processes, such as progressive cell death. An ultrahigh-resolution optical coherence microscopy (UHR-OCM) system with [Formula: see text] axial resolution and [Formula: see text] transverse resolution was developed to evaluate seizure-induced neuronal injury in organotypic rat hippocampal cultures. The capability of UHR-OCM to visualize cells in neural tissue was confirmed by comparison of UHR-OCM images with confocal immunostained images of the same cultures. In order to evaluate the progression of neuronal injury, UHR-OCM images were obtained from cultures on 7, 14, 21, and 28 days in vitro (DIVs). In comparison to DIV 7, statistically significant reductions in three-dimensional cell count and culture thickness from UHR-OCM images were observed on subsequent time points. In cultures treated with kynurenic acid, significantly less reduction in cell count and culture thickness was observed compared to the control specimens. These results demonstrate the capability of UHR-OCM to perform rapid, label-free, and nondestructive evaluation of neuronal death in organotypic hippocampal cultures. UHR-OCM, in combination with three-dimensional tissue cultures, can potentially prove to be a promising tool for high-throughput screening of drugs targeting various disorders.