How to find real-world applications for compressive sensing

The potential of compressive sensing (CS) has spurred great interest in the research community and is a fast growing area of research. However, research translating CS theory into practical hardware and demonstrating clear and significant benefits with this hardware over current, conventional imaging techniques has been limited. This article helps researchers to find those niche applications where the CS approach provides substantial gain over conventional approaches by articulating lessons learned in finding one such application; sea skimming missile detection. As a proof of concept, it is demonstrated that a simplified CS missile detection architecture and algorithm provides comparable results to the conventional imaging approach but using a smaller FPA. The primary message is that all of the excitement surrounding CS is necessary and appropriate for encouraging our creativity but we all must also take off our "rose colored glasses" and critically judge our ideas, methods and results relative to conventional imaging approaches.Comment: 10 page

Optimal Coded Diffraction Patterns for Practical Phase Retrieval

Phase retrieval, a long-established challenge for recovering a complex-valued signal from its Fourier intensity measurements, has attracted significant interest because of its far-flung applications in optical imaging. To enhance accuracy, researchers introduce extra constraints to the measuring procedure by including a random aperture mask in the optical path that randomly modulates the light projected on the target object and gives the coded diffraction patterns (CDP). It is known that random masks are non-bandlimited and can lead to considerable high-frequency components in the Fourier intensity measurements. These high-frequency components can be beyond the Nyquist frequency of the optical system and are thus ignored by the phase retrieval optimization algorithms, resulting in degraded reconstruction performances. Recently, our team developed a binary green noise masking scheme that can significantly reduce the high-frequency components in the measurement. However, the scheme cannot be extended to generate multiple-level aperture masks. This paper proposes a two-stage optimization algorithm to generate multi-level random masks named $\textit{OptMask}$ that can also significantly reduce high-frequency components in the measurements but achieve higher accuracy than the binary masking scheme. Extensive experiments on a practical optical platform were conducted. The results demonstrate the superiority and practicality of the proposed $\textit{OptMask}$ over the existing masking schemes for CDP phase retrieval

Design and Analysis of a Spatial Neutron Modulator for Neutron Imaging via Compressive Sensing

The ability to characterize materials is an important aspect when performing any sort of materials science experiment. When performing studies where understanding the molecular-level characteristics are critical such as for molecular bonding in batteries or complex polymers, analysis techniques such as vibrational spectroscopy are often used. At the Spallation Neutron Source at Oak Ridge National Laboratory the VISION instrument performs vibrational spectroscopy using neutrons. The reason for utilizing neutrons includes aspects such as increased penetration depth into the sample and high sensitivity; however, currently it is only possible to perform bulk sample measurements. By utilizing a technique known as compressive sensing it will be made possible to map material properties and characteristics across an entire sample.This thesis discusses the design process for creating a spatial neutron modulator that will allow for compressive sensing to be used within VISION as well as the creation of a testable prototype and analysis of the prototype’s performance. In order to test the performance of the system as well as the compressive sensing algorithm the prototype is tested using an optical set-up. This approach allows for the concepts to be tested on a cheaper scale to ensure feasibility of the design and compressive sensing algorithm prior to the creation of the final system

듀얼미러 라이다 이미징을 위한 타이밍이 고려된 샘플링 알고리즘

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. Lee, Hyuk-Jae.In recent years, active sensor technologies such as light detection and ranging (LIDAR) have been intensively studied in theory and widely adopted in many applications, i.e., self-driving cars, robotics and sensing. Generally, the spatial resolution of a depth-acquisition device, such as a LiDAR sensor, is limited because of a slow acquisition speed. To accurately reconstruct a depth image from a limited spatial resolution, a two-stage sampling process has been widely used. However, two-stage sampling uses an irregular sampling pattern for the sampling operation, which requires a large amount of computation for reconstruction. A mathematical formulation of a LiDAR system demonstrates that the existing two-stage sampling does not satisfy its timing constraint for practical use. Therefore, designing a LiDAR system with an efficient sampling algorithm is a significant technological challenge. Firstly, this thesis addresses the problem of adopting the state-of-art laser marking system of a dual-mirror deflection scanner when creating a high-definition LIDAR system. Galvanometer scanners are modeled and parameterized based on concepts of their controllers and the well-known raster scanning method. The scanning strategy is then modeled and analyzed considering the physical scanning movement and the minimum spanning tree. From this analysis, the link between the quality of the captured image of a field of view (FOV) and the scanning speed is revealed. Furthermore, sufficient conditions are derived to indicate that the acquired image fully covers the FOV and that the captured objects are well aligned under a specific frame rate. Finally, a sample LIDAR system is developed to illustrate the proposed concepts. Secondly, to overcome the drawbacks of two-stage sampling, we propose a new sampling method that reduces the computational complexity and memory requirements by generating the optimal representatives of a sampling pattern in down-sample data. A sampling pattern is derived from a k-NN expanding operation from the downsampled representatives. The proposed algorithm is designed to preserve the object boundary by restricting the expansion-operation only to the object boundary or complex texture. In addition, the proposed algorithm runs in linear-time complexity and reduces the memory requirements using a down-sampling ratio. Experimental results with Middlebury datasets and Brown laser-range datasets are presented. Thirdly, state-of-the-art adaptive methods such as two-step sampling are highly effective while addressing indoor, less complex scenes at a moderately low sampling rate. However, their performance is relatively low in complex on-road environments, particularly when the sampling rate of the measuring equipment is low. To address this problem, this thesis proposes a region-of-interest-(ROI)-based sampling algorithm in on-road environments for autonomous driving. With the aid of fast and accurate road and object detection algorithms, particularly those based on convolutional neural networks (CNNs), the proposed sampling algorithm utilizes the semantic information and effectively distributes samples in road, object, and background areas. Experimental results with KITTI datasets are presented.최근 LIDAR (light detection and ranging)와 같은 능동적 센서 기술은 이론적으로도 집중적으로 연구되었고, 자율주행차, 로봇, 센싱 등 다양한 응용 분야에 널리 사용되고 있다. 일반적으로 LiDAR 센서와 같은 심도측정장치는 느린 속도 때문에 공간적 해상도가 제한된다. 제한된 공간적 해상도로부터 심도 이미지를 정확하게 재구성하기 위해서 2단계 샘플링 방법이 널리 사용되고 있다. 그러나 2단계 샘플링은 불규칙적인 샘플링 패턴으로 샘플링을 하기 때문에, 재구성 과정에 많은 양의 연산이 필요하다. LiDAR 시스템을 수학적인 모델을 사용하여 분석하였을 때, 기존의 2단계 샘플링은 실용적으로 사용되기 위한 타이밍 제약 조건을 만족하지 못함을 확인하였다. 따라서 효율적인 샘플링 알고리즘을 사용하는 LiDAR 시스템을 설계하는 것은 중요한 기술적 과제이다. 첫째, 본 논문은 최신의 레이저 마킹 시스템을 dual-mirror 스캐너에 적용하여 고해상도 LiDAR 시스템을 만드는 문제를 다룬다. Galvanometer 스캐너 컨트롤러와 잘 알려진 래스터 스캔 방법에 기초하여 Galvanometer 스캐너를 모델링, 매개변수화한다. 그리고 물리적인 스캐닝 움직임과 최소 신장 트리를 고려하여 스캐닝 방법을 모델링하고 분석한다. 분석으로부터 원하는 FOV (field of view)로 캡쳐된 이미지의 품질과 스캐닝 속도 사이의 관계를 밝혔다. 또한 획득된 이미지가 FOV를 완전히 표현하며, 캡쳐된 object들이 특정 프레임 레이트에서 잘 정렬됨을 나타내는 충분조건을 유도하였다. 마지막으로 제안된 개념을 확인하기 위해 샘플 LIDAR 시스템을 개발하였다. 둘째, 2단계 샘플링의 단점을 극복하기 위해, 다운 샘플 데이터에서 샘플링 패턴의 최적 표현을 생성함으로써 연산 복잡도와 메모리 요구량을 줄일 수 있는 새로운 샘플링 방법을 제안한다. 샘플링 패턴은 다운 샘플된 표현의 k-NN 확장 연산으로부터 도출된다. 제안된 방법은 물체 경계 또는 복잡한 텍스처에 한해서 확장연산을 수행함으로써 물체 경계를 보존하도록 설계되었다. 또한 제안하는 방법은 선형적인 시간 복잡도로 동작하며 다운 샘플링 비율을 이용하여 메모리 요구량을 줄인다. Middlebury 데이터셋과 Brown laser-range 데이터셋을 사용한 실험 결과가 제시된다. 셋째, 2단계 샘플링과 같은 최신의 적응적 방법들은 비교적 낮은 샘플링 레이트로 실내의 복잡하지 않은 장면들을 처리하는 데 매우 효과적이다. 그러나 복잡한 도로 환경에서는, 특히 측정 장비의 샘플링 레이트가 낮은 경우에, 해당 방법들의 성능이 상대적으로 떨어진다. 이 문제를 해결하기 위해 본 논문은 자율주행을 위한 도로 환경에서의 ROI (region-of-interest) 기반 샘플링 알고리즘을 제안한다. 제안된 샘플링 알고리즘은 CNN (convolutional neural network) 기반의 빠르고 정확한 도로 및 물체 감지 알고리즘을 사용하여, semantic 정보를 활용하고 도로, 물체, 배경 영역에 샘플들을 효과적으로 분배한다. KITTI 데이터셋을 사용한 실험 결과가 제시된다.Abstract i Table of Contents iii List of Figures vii List of Tables xi Chapter 1: Introduction １ 1.1. Overview １ 1.2. Scope and contributions ２ 1.3. Thesis Outlines ３ Chapter 2: Related work ４ 2.1. LiDAR sensors ４ 2.2. Sampling ６ 2.2.1. Sampling problem definition ６ 2.2.2. Sampling model ７ 2.2.3. Oracle Random sampling (Gradient-based sampling) ８ 2.3. Reconstruction ９ Chapter 3: Dual-Mirror LiDAR １１ 3.1. Introduction １１ 3.1.1. Related work １２ 3.2. Modelling a controller of dual-mirror scanners １３ 3.2.1. Dual-mirror scanners １３ 3.2.2. Controller Model １５ 3.2.2.1. FOV representation １５ 3.2.2.2. Timing constraints １６ 3.2.2.3. Maximum Speed of LiDAR scanners １７ 3.3. LiDAR scanning optimization problem １８ 3.3.1. Scanning Problem １９ 3.3.2. Optimal scanning pattern ２０ 3.3.2.1. Grid-graph representation of Field of View ２０ 3.3.2.2. Optimal scanning pattern ２１ 3.3.2.3. Combining an optimal sampling pattern with timing constraints ２4 3.4. LiDAR system Prototype ３０ 3.4.1. System overview ３０ 3.4.2. Speed evaluation ３２ 3.4.3. Subjective Evaluation ３３ 3.4.4. Accuracy Evaluation ３６ Chapter 4: Sampling for Dual-Mirror LiDAR: Sampling Model and Algorithm ３８ 4.1. Introduction ３８ 4.2. Sampling Model for Dual-Mirror LiDAR ４１ 4.2.1. Timing constraint ４１ 4.2.2. Memory-space constraint ４５ 4.2.3. New sampling problem with constraints ４７ 4.3. Proposed sampling Algorithm and Its Properties ４８ 4.3.1. Downsampling and k-NN expanding operator ４８ 4.3.2. Proposed Sampling Algorithm with k-NN Expanding ５２ 4.3.3. Example with Synthetic Data ５７ 4.3.4. Proposed sampling algorithm with interpolation ５９ 4.3.5. Timing and memory constraints ６２ 4.3.5.1. Timing constraint ６２ 4.3.5.2. Memory constraint ６３ 4.4. Experimental results ６４ 4.4.1. Comparison on the conventional sampling problem ６５ 4.4.1.1. Subjective comparison ６５ 4.4.1.2. Quantitative comparison ６５ 4.4.2. Comparison on the new sampling problem for LiDAR ６９ 4.4.2.1. Compression ratios ６９ 4.4.2.2. Quantitative evaluation with Peak-signal-to-noise-ratio ７０ 4.4.2.3. Quantitative evaluation with Percentages of bad pixels ７２ 4.4.3. Subjective evaluation ７７ 4.4.4. Proposed grid sampling and grid sampling method ７９ 4.4.4.1. Middlebury datasets ７９ 4.4.4.2. Brown Laser range datasets ８０ Chapter 5: ROI-based LiDAR Sampling in On-Road Environment for Autonomous Driving ８４ 5.1. Introduction ８４ 5.2. Proposed ROI-based sampling algorithm ８７ 5.2.1. Motivating example ８７ 5.2.2. ROI-based Sampling Problem ９１ 5.2.3. Proposed ROI-based sampling algorithm ９３ 5.2.4. Practical considerations ９４ 5.2.5. Distortion optimization problem ９５ 5.3. Experimental results ９６ 5.3.1. Datasets ９６ 5.3.2. Evaluation with different parameters ９９ 5.3.3. Object and quantitative comparisons １０２ Chapter 6: Implementation Issues １０８ 6.1. Implementation of gradient-based sampling １０８ 6.2. System overview １１１ Chapter 7: Conclusion １１３ References １１５ 초록 １２４Docto

Signal processing for microwave imaging systems with very sparse array

This dissertation investigates image reconstruction algorithms for near-field, two dimensional (2D) synthetic aperture radar (SAR) using compressed sensing (CS) based methods. In conventional SAR imaging systems, acquiring higher-quality images requires longer measuring time and/or more elements in an antenna array. Millimeter wave imaging systems using evenly-spaced antenna arrays also have spatial resolution constraints due to the large size of the antennas. This dissertation applies the CS principle to a bistatic antenna array that consists of separate transmitter and receiver subarrays very sparsely and non-uniformly distributed on a 2D plane. One pair of transmitter and receiver elements is turned on at a time, and different pairs are turned on in series to achieve synthetic aperture and controlled random measurements. This dissertation contributes to CS-hardware co-design by proposing several signal-processing methods, including monostatic approximation, re-gridding, adaptive interpolation, CS-based reconstruction, and image denoising. The proposed algorithms enable the successful implementation of CS-SAR hardware cameras, improve the resolution and image quality, and reduce hardware cost and experiment time. This dissertation also describes and analyzes the results for each independent method. The algorithms proposed in this dissertation break the limitations of hardware configuration. By using 16 x 16 transmit and receive elements with an average space of 16 mm, the sparse-array camera achieves the image resolution of 2 mm. This is equivalent to six percent of the λ/4 evenly-spaced array. The reconstructed images achieve similar quality as the fully-sampled array with the structure similarity (SSIM) larger than 0.8 and peak signal-to-noise ratio (PSNR) greater than 25 --Abstract, page iv