131 research outputs found
Laser source, image processing and fast imaging technology for opticalcoherence tomography
No full textpublished_or_final_versionElectrical and Electronic EngineeringMasterMaster of Philosoph
Efficient finite-difference schemes in thermal analysis and inverse lithography for integrated circuit manufacturing
No full textpublished_or_final_versionElectrical and Electronic EngineeringDoctoralDoctor of Philosoph
Optical coherence tomography : from system design to spectroscopic applications
No full textOptical coherence tomography (OCT), as a newly developed imaging modality, has attracted significant attention due to its capability to obtain the cross-sectional information of biological tissues in a non-invasive way, with resolution in the range of several micrometers. The third-generation swept source OCT (SS-OCT), is superior in the speed, imaging range and signal-to-noise ratio (SNR) compared with the previous time-domain OCT (TD-OCT) and spectral domain OCT (SD-OCT), and therefore forms our research focus.
In this thesis, for the first time, I investigate the deployment of Fourier domain mode-locked (FDML) swept laser by utilizing the bismuth-based erbium doper fiber (Bi-EDF) with a sweeping bandwidth of ~ 81nm achieved. Following, fiber Raman amplifier (FRA) is also investigated by employing multiple Raman pumps. The tuning range is ~111.8nm, which is much larger than the previous reported Raman pumped FDML in the 1550nm region. Imaging was performed to validate the feasibility of the proposed schemes for the SS-OCT applications, respectively.
In addition to the FDML swept laser cavity design, speckle noise reduction is also of great importance in OCT, which can significantly improve the visibility of the obtained OCT images. I demonstrate two different speckle reduction methods for OCT applications, which are superior in suppressing speckle noise and reserving the one-dimensional (1D) and two-dimensional (2D) signal information, respectively. Applying the proposed wavelet domain compounding (WDC) and contourlet shrinkage method to despeckle the OCT images, the visibility of the OCT images was significantly improved, with negligible edge preservation compromise.
Spectroscopic information is also of interest to many researchers as it provides additional spectroscopic contrast, which on one hand, will improve the visualization of the images, and on the other hand, will enable the classification of different tissue types and help the process of discrimination between invasive and noninvasive tumors. Compared with our previous reported work about dual-band spectroscopic OCT based on optical parametric amplifier (OPA) to generate another idler band, which will be used as the second band for dual-band spectroscopic analysis, I further extend the dual-band spectroscopic OCT to the endoscopic applications, and investigate the dual-band FDML swept laser configuration based on a custom-designed dual-channel driver to synchronize the two different wavelength bands, centered at 1310 and 1550 nm, respectively. OCT Images for different bands are captured and post-processed by coding the spectral difference in different colors.
In short, in this thesis, the investigations of OCT range from system design, speckle reduction to the spectroscopic applications. All these research efforts will extend the current FDML techniques for a wide range of SS-OCT applications. These schemes may be useful in OCT swept laser source build up, speckle noise reduction, and the extension of spectroscopic analysis.published_or_final_versionElectrical and Electronic EngineeringDoctoralDoctor of Philosoph
Neuromorphic event sensing for laser speckle imaging and autofocusing microscopy
No full textImaging systems primarily rely on photodetectors to monitor and capture light. Conventional image sensors such as charge-coupled device (CCD) and complementary metal oxide semiconductor (CMOS) are almost always used in various imaging systems to record the absolute intensity of every pixel. However, such sensing scheme is not good enough to carry dynamic information, especially in some imaging systems that are designed for moving scenes. Inspired by biology retinas, neuromorphic event sensor is emerging as a promising sensing paradigm where each pixel works independently to detect brightness changes at high temporal resolution and
low latency. The output is a stream of asynchronous event data that carriesonly binary information of the brightness changes, together with the pixel location and timestamp. Such unique characteristics drastically reduce the amount of generated data and improve the efficiency in both data acquisition and subsequent computation. The purpose of this dissertation is to harness the neuromorphic event sensing paradigm to cope with the challenges in laser speckle imaging (LSI) and autofocusing microscopy.
First, we tackle the problem of dynamic laser speckle analysis (DLSA) which is a non-contact method to estimate the dynamic levels of the inspected objects. The sample surface is illuminated using a coherent light source and the results can be obtained by analyzing the series of reflected speckle patterns. As the first reported work in DLSA with neuromorphic event sensing, we present the proof-of-principle results and demonstrate the superiority of our method compared with conventional frame-based image sensors under different dynamic levels. Two evaluation metrics are proposed in this work to efficiently analyze the event form of speckle patterns.
Second, we propose a neuromorphic LSI method to estimate micro motion. Traditional LSI techniques rely on identifying changes from the timecorrelated intensity speckle patterns, where a lot of redundant data of the static speckles without motion information will also be recorded and the motion cues are inevitably lost during the “blind” time interval between successive frames. In our proposed method, the reflected high frequency laser speckle patterns are captured using a neuromorphic event sensor on the order of microseconds. We present two data processing strategies that are based on speckle correlation and block matching to estimate micro motion from the event-based laser speckles. Experimental results demonstrate the validity and robustness of our method in a wide range of motion speeds with less than 2% error rate.
Third, we focus on the autofocusing problem in microscopy. Rapid autofocusing is essential for many microscopic imaging applications. Existing methods either require complicated hardware implementations or slow z-stack image acquisition. To cope with this problem, we develop a new approach to achieve fast autofocusing by detecting the brightness variation in the axial diffraction using the neuromorphic event sensing. A simple yet efficient autofocusing system is tailored for fast acquisition and processing
of the non-redundant event data, allowing for autofocusing in only tens of milliseconds, which is thousands of times faster than current technologies. Experimental results show a substantial performance improvement and capability for biopsy specimen inspections.published_or_final_versionElectrical and Electronic EngineeringDoctoralDoctor of Philosoph
Automatic source camera identification by lens aberration and JPEG compression statistics
No full textpublished_or_final_versionabstractElectrical and Electronic EngineeringMasterMaster of Philosoph
Binary image restoration by positive semidefinite programming and signomial programming
No full textpublished_or_final_versionabstractElectrical and Electronic EngineeringMasterMaster of Philosoph
Digital holography with advanced autofocusing and reconstruction algorithms
No full textDigital holography (DH) is a rapidly developing field that has drawn tremendous attention from both research and commercial points of view. It can optically record a three-dimensional (3D) scene as a 2D digital hologram, and reconstruct the object with numerical back-propagation. Due to the dynamic, label-free and phase-contrast imaging, DH has been widely used in 4D biological microscopy and surface topography. Yet, autofocusing and Fresnel propagation-based algorithms are essential steps for reconstruction. The purpose of this dissertation is to handle the autofocusing in DH, and to harness the recent emerging learning-based algorithm to cope with the challenges arising with digital holographic reconstruction.
First, although DH allows post-processing on holograms to reconstruct multi-focus images, it suffers from defocus noise in numerical reconstruction. A method that can achieve extended focused imaging (EFI), in which all sections are in-focus and sharp, and can reconstruct a depth map (DM) which presents true depths of the individual sections, of a 3D scene is demonstrated. A depth-from-focus algorithm is first used to create the DM for each pixel based on entropy minimization. Then, computationally, the EFI of the whole 3D object is obtained with the help of DM.
Second, since in DH a 3D object that consists of multiple discrete sections may have overlapping regions with each other, this situation brings more challenging to autofocusing. To deal with the overlapping, a focus metric based on the Lp norm of the eigenvalues of structure tensor matrix is proposed. The efficacy of the proposed autofocusing method on non-overlapping and overlapping cases is verified by holographically recording a multi-sectional object.
Furthermore, conventional autofocusing, which is tackled by evaluating the sharpness of sequential reconstructed images within an estimated range using a focus metric, while effective, is computationally demanding and time-consuming. To cope with this problem, the autofocusing is cast as a regression, in which the focal distance is regarded as the response of a raw hologram. Therefore, estimating the object’s distance turns into predicting the hologram, which is solved by training a deep convolutional neural network with substantial holograms and true responses acquired a priori. It is shown that, by doing so, even in the absence of knowing the physical parameters, reconstructing an image stack for autofocusing is avoided, leading to fast prediction.
Fourth, although conventional reconstruction algorithms are effective, filtering operation, autofocusing and prior knowledge such as the pixel pitch and the source wavelength are inevitable and consume more time. While for phase-contrast imaging, the phase aberration has to be compensated with additional hardware or algorithm, and subsequently an unwrapping step, which is sensitive to noise and distortion, follows to recover the true phase. Besides, for a multi-sectional object, the EFI and DM are desired for many applications, but current approaches tend to be computationally demanding. Thus, an end-to-end deep learning framework is proposed to tackle these holographic reconstruction problems. Through this data-driven approach, it is demonstrated that it is possible to reconstruct a noise-free image that does not require any prior knowledge directly from a raw hologram.published_or_final_versionElectrical and Electronic EngineeringDoctoralDoctor of Philosoph
Synergy of dynamic perception and static vision in neuromorphic imaging
No full textThe human retina, stimulated by light, transmits electrical impulses to the brain in which image formation and message acquisition occur. Neuromorphic imaging is a bio-inspired solution that revolutionizes the way visual scenes are captured — by asynchronous streaming events instead of synchronous full images. Compared with frame imaging, it proudly enjoys several remarkable features, such as microsecond-level temporal resolution that enables blur-free recordings of fleeting motion moments, a high dynamic range where there is a clearer observation under dazzling sunlight or in dark midnight, along with minimal latency and low power for seamless integration into portable devices of restrictive computing resources. Nevertheless, this modality has limitations, and the resulting event stream is also a challenge for humans and machines that are accustomed to comprehending static images. We thus have a strong motivation to prompt the synergistic interaction between the two information sources. In this thesis, we bridge dynamic perception with static vision via the proposed approaches to leverage the strength of each, such that elevating imaging quality and paving the way for intelligent neuromorphic systems.
The sensitivity of neuromorphic imaging comes at the cost of susceptibility to interference, leading to informative events triggered along with a storm of noise, which compromises the accuracy of subsequent evaluations. We suggest a blind denoising approach that couples event priors with density statistics for noise removal in a sub-quadratic fashion. The refined output greatly enhances downstream reasoning tasks.
In privacy-sensitive scenarios, the use of neuromorphic imaging may raise security concerns. We introduce an event encryption method based upon event denoising, where events are inversely filled with noise until being obfuscated. The encrypted ones can thwart attacks that harness intensity reconstruction and high-level vision analysis, endowing systems with more robust privacy-preserving capabilities.
Neuromorphic imaging fails to capture low-frequency signal, which is inferior in blurry images due to frame imaging with a low frame rate. A unifying framework, which reaches a maximal exploitation of the complementary nature of the two imaging results, is proposed to jointly reconstruct blur-free images and noise-free events in parallel. Evaluated on a rich range of real samples, this solution brings a convincing quality improvement.
Also, it has limited spatial resolution and fails to deliver richness of visual clarity. We present a self-supervised neuromorphic super-resolution prototype that is adaptive to per input without lengthy training on side knowledge, showing a higher level of practicality and flexibility in the present situation where high-resolution devices remain imperfect and expensive.
Lastly, we rethink event-based representations that are seamlessly compatible with learning platforms, and move forward to event graphs beyond images. With a compact memory footprint, such a synchronous graph expression enables fragmented inference on limited events for efficient recognition, which is more friendly to edge computing and mobile applications where computational resources are critical.
In closing, we provide a summary of our research endeavors and outline a prospect for the synergy of neuromorphic imaging and machine intelligence.published_or_final_versionElectrical and Electronic EngineeringDoctoralDoctor of Philosoph
Next generation quantitative phase imaging at versatile wavelengths
No full textQuantitative phase imaging (QPI) techniques are emerged as a major modality in biomedical imaging, because of their ability to provide label free quantitative phase information of biological samples. QPI provides extra image contrast by measuring the total phase difference information, especially in optically transparent samples, that is useful in further quantitative studies of sample. Interferometric techniques are widely used in phase imaging modalities such as digital holography, while their phase retrieval sensitivities can be affected by environmental conditions and vibrations. Mostly reported QPI techniques use the visible wavelength range of light source which suffers from less penetration depth and generates a speckle noise from the scattering sample that drastically degrades the image quality. Although there are different techniques based on image or signal processing such as; wave front shaping, structured light illumination and computational imaging which were reported in recent years to overcome the imaging through scattering media problem.
Another way to reduce the speckle noise and increase the penetration depth in the sample by using longer wavelength range source. It has been shown that the 2-μm wavelength regime can provide higher penetration depth and less scattering from the sample which improves the image quality. Based on this concept here we have demonstrated a spectrally encoded QPI microscopy using 2-μm fiber laser.
Several techniques are available to retrieve phase information using a non-interferometric optical system that only measures intensity images of the sample. These techniques are mainly iterative and based on multi-plane intensity measurements but simple in implementation. Due to simplicity in implementation, they are less sensitive to environmental conditions and vibrations.
It has also been demonstrated in various publications that the laser source wavelength in QPI plays an important role in the imaging system’s performance. It has been shown that in the ultraviolet regime absorption increases with shorter wavelengths for proteins, DNA, and other molecules. In the infrared regime, absorption increases with longer wavelengths due to presence of the water content in the tissues. Moreover, near 650 nm to 1.3 μm wavelength window is called as therapeutic or diagnostic window where absorption is less. In this diagnostic regime near 1-μm wavelength window, the absorption is very small for various biological tissues, making it suitable for biomedical imaging for diagnostic or therapeutic applications. Here we have proposed and demonstrated to explore the advantages of this therapeutic wavelength window together with non-interferometric QPI for the measurement of refractive index, stress-strain on the cell membrane, mass, and volume of the sample.
Moreover, two-photon absorption process also provides significant advantages, such as higher depth selectivity, high resolution, less photo-toxicity, and better localization in optical imaging. Here we are also leveraging the two-photon absorption for 1-μm wavelength regime QPI to improve its performance and in other words, demonstrated phase sensitive two-photon microscopy (PS2PM) or two photon QPI microscopy.published_or_final_versionElectrical and Electronic EngineeringDoctoralDoctor of Philosoph
Novel optical sources and signal processing methods for optical imaging modalities
No full textOptical coherence tomography (OCT) and optical microscopy (including linear and nonlinear optical microscopy) are two important optical imaging modalities for biomedical imaging applications. An optical imaging system generally consists of an optical source, a light-sample interaction mechanism, and a signal processing unit. In this thesis, a type of novel optical sources and optical signal processing methods are developed to improve system performance of OCT and optical microscopy in terms of imaging speed, imaging contrast, and imaging range. These improvements are achieved through increasing the repetition rate of the optical source, enhancing detection sensitivity, and reducing optical signal bandwidth by leveraging broadband high-energy fiber mode-locked laser, fiber optical parametric amplifier, and dual optical frequency combs.
In optical source part, an ultrafast all-fiber inertial-free swept source built upon a broadband fiber mode-locked laser in conjunction with time-stretch technique was demonstrated. A swept source OCT was constructed based on this light source whose repetition rate (44.5 MHz) was one order of magnitude higher than that in conventional OCT systems. Besides, a high-energy (3.9 nJ) fiber mode-locked laser at 1.6-µm window was built for a wavelength-tunable optical source with wavelength tuning range from 1.6 µm to 1.8 µm through soliton self-frequency shift in optical fiber. In addition, a high energy pulse generation scheme for three-photon fluorescence microscope by using this fiber laser and a chirped pulse amplifier was also exhibited. These fiber sources at L-band and longer wavelength can explore a wider scope in deep bio-tissue imaging area for lower water absorption.
In optical signal processing part, to enhance detection sensitivity, a scheme by using fiber optical parametric amplifier as the pre-amplifier before the photodetector in optical imaging systems was proposed. The effectiveness by using this sensitivity enhancement scheme was proved in theory based on a semi-classical model, and it was also verified experimentally in a swept-source OCT and time-stretch microscope. The sensitivity enhancement capability with amplified signal band (single band) is determined by the gain of fiber optical parametric amplifier, and 3-dB sensitivity can be further improved by using both amplified signal and phase-conjugated idler together (dual band). This sensitivity enhancement scheme can be potentially applied in the scenarios where ultrafast broadband signal at low-power level is being handled.
To reduce optical signal bandwdith at signal processing section, a type of dual optical frequency combs were developed based on electro-optic modulators and nonlinear devices (nonlinear optical loop mirror and a nonlinear amplified loop mirror). A video-rate dual-comb OCT with centimeter imaging range was demonstrated based on this dual-comb source. By down-converting the interference signal from optical domain to radio-frequency domain through dual-comb beating, the down-converted bandwidth of the interference signal in dual-comb OCT was at least two orders of magnitude lower than that in conventional OCT systems.
In summary, a high performance optical imaging system normally relies on advanced optical sources and signal processing methods. Fiber mode-locked laser, fiber optical parametric amplifier, and dual optical frequency combs provide powerful and versatile solutions to construct high performance OCT and optical microscopy systems.published_or_final_versionElectrical and Electronic EngineeringDoctoralDoctor of Philosoph
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