Phase diversity is a technique for obtaining estimates of both the object and the phase, by exploiting the simultaneous collection of two (or more) short-exposure optical images, one of which has been formed by further blurring the conventional image in some known fashion. This paper concerns a fast computational algorithm based upon a regularized variant of the Gauss-Newton optimization method for phase diversity-based estimation when a Gaussian likelihood fit-to-data criterion is applied. Simulation studies are provided to demonstrate that the method is remarkably robust and numerically efficient. Keywords: phase diversity, blind deconvolution, phase retrieval, quasi-Newton methods 1. INTRODUCTION Phase diversity-based blind deconvolution is a technique for obtaining estimates of both the object and the phase in optical image deblurring. It involves the simultaneous collection of two (or more) short-exposure images. One of these is the object that has been blurred by unknown aberra...

We prove computability and complexity results for an original model of computation called the continuous space machine. Our model is inspired by the theory of Fourier optics. We prove our model can simulate analog recurrent neural networks, thus establishing a lower bound on its computational power. We also define a \Theta (log_2 n) unordered search algorithm with our model.

...................................................................................................................................... viii List of publications .......................................................................................................................ix Chapter 1: Introduction..................................................................................................................1 1.1 Scope and overall research contribution..............................................................................1 1.2 Motivation............................................................................................................................2 1.2.1 The interconnect problem .............................................................................................2 1.2.2 Capabilities and limitations of electrical interconnects................................................4 1.2.3 Advantages of optical interconnects ......................................

This paper introduces an optical solution to (bounded-length input instances of) an NP-complete problem called the traveling salesman problem using a pure optical system. The solution is based on the multiplication of a binary-matrix, representing all feasible routes, by a weight-vector, representing the weights of the problem. The multiplication of the binary-matrix by the weight-vector can be implemented by any optical vector-matrix multiplier. In this paper, we show that this multiplication can be obtained by an optical correlator. In order to synthesize the binary-matrix, a unique iterative algorithm is presented. This algorithm synthesizes an N-node binary-matrix using rather small number of vector duplications from the (N−1)-node binary-matrix. We also show that the algorithm itself can be implemented optically and thus we ensure the entire optical solution to the problem. Simulation and experimental results prove the validity of the optical method.

This paper presents a camera that samples the 4D light field on its sensor in a single photographic exposure. This is achieved by inserting a microlens array between the sensor and main lens, creating a plenoptic camera. Each microlens measures not just the total amount of light deposited at that location, but how much light arrives along each ray. By re-sorting the measured rays of light to where they would have terminated in slightly different, synthetic cameras, we can compute sharp photographs focused at different depths. We show that a linear increase in the resolution of images under each microlens results in a linear increase in the sharpness of the refocused photographs. This property allows us to extend the depth of field of the camera without reducing the aperture, enabling shorter exposures and lower image noise. Especially in the macrophotography regime, we demonstrate that we can also compute synthetic photographs from a range of different viewpoints. These capabilities argue for a different strategy in designing photographic imaging systems. To the photographer, the plenoptic camera operates exactly like an ordinary hand-held camera. We have used our prototype to take hundreds of light field photographs, and we present examples of portraits, high-speed action and macro close-ups.

this paper. Key words: auto-focus; disc testbed; optical discs; standard deviation. Accepted: July 20, 2000 Available online: http://www.nist.gov/jres 1. Introduction In an optical vision system, an autofocusing element plays an important role. An objective lens collects light from the object and makes a clear image at the focal plane if the distance between the object and the objective lens is perfectly controlled to meet the focusing condition. A feedback signal is always needed for the controlling system. There are many kinds of feedback signal that can be used for this purpose in different systems. For example, an astigmatic field lens and a quad-detector (hardware) are used to create a focusing feedback signal in Compact Disc (CD) drives [1]. For digital image signals grabbed via a CCD camera, the feedback signal for autofocusing can be extracted from the image itself by software using Fourier transforms [2]. Ac

Abstract — Compressive imaging (CI) is a natural branch of compressed sensing (CS). Although a number of CI implementations have started to appear, the design of efficient CI system still remains a challenging problem. One of the main difficulties in implementing CI is that it involves huge amounts of data, which has far-reaching implications for the complexity of the optical design, calibration, data storage and computational burden. In this paper, we solve these problems by using a twodimensional separable sensing operator. By so doing, we reduce the complexity by factor of 10 6 for megapixel images. We show that applying this method requires only a reasonable amount of additional samples.

Compressive imaging (CI) is a natural branch of compressed sensing (CS). One of the main difficulties in implementing CI is that, unlike many other CS applications, it involves huge amount of data. This data load has extensive implications for the complexity of the optical design, for the complexity of calibration, for data storage requirements. As a result, practical CI implementations are mostly limited to relative small image sizes. Recently we have shown that it is possible to overcome these problems by using a separable imaging operator. We have demonstrated that separable imaging operator permits CI of megapixel size images and we derived a theoretical bound for oversampling factor requirements. Here we further elaborate the tradeoff of using separable imaging operator, present and discuss additional experimental results.

iii ivAcknowledgments I feel tremendously lucky to have had the opportunity to work with Pat Hanrahan, Marc Levoy and Mark Horowitz on the ideas in this dissertation, and I would like to thank them for their support. Pat instilled in me a love for simulating the flow of light, agreed to take me on as a graduate student, and encouraged me to immerse myself in something I had a passion for.Icouldnothaveaskedforafinermentor.MarcLevoyistheonewhooriginallydrewme to computer graphics, has worked side by side with me at the optical bench, and is vigorously carrying these ideas to new frontiers in light field microscopy. Mark Horowitz inspired me to assemble my camera by sharing his love for dismantling old things and building new ones. I have never met a professor more generous with his time and experience. I am grateful to Brian Wandell and Dwight Nishimura for serving on my orals committee. Dwight has been an unfailing source of encouragement during my time at Stanford. I would like to acknowledge the fine work of the other individuals who have contributed to this camera research. Mathieu Brédif worked closely with me in developing the simulation system, and he implemented the original lens correction software. Gene Duval generously

Quantitative phase imaging with scanning holographic microscopy: an experimental assesment