Modeling and applications of the focus cue in conventional digital cameras

El enfoque en cámaras digitales juega un papel fundamental tanto en la calidad de la imagen como en la percepción del entorno. Esta tesis estudia el enfoque en cámaras digitales convencionales, tales como cámaras de móviles, fotográficas, webcams y similares. Una revisión rigurosa de los conceptos teóricos detras del enfoque en cámaras convencionales muestra que, a pasar de su utilidad, el modelo clásico del thin lens presenta muchas limitaciones para aplicación en diferentes problemas relacionados con el foco. En esta tesis, el focus profile es propuesto como una alternativa a conceptos clásicos como la profundidad de campo. Los nuevos conceptos introducidos en esta tesis son aplicados a diferentes problemas relacionados con el foco, tales como la adquisición eficiente de imágenes, estimación de profundidad, integración de elementos perceptuales y fusión de imágenes. Los resultados experimentales muestran la aplicación exitosa de los modelos propuestos.The focus of digital cameras plays a fundamental role in both the quality of the acquired images and the perception of the imaged scene. This thesis studies the focus cue in conventional cameras with focus control, such as cellphone cameras, photography cameras, webcams and the like. A deep review of the theoretical concepts behind focus in conventional cameras reveals that, despite its usefulness, the widely known thin lens model has several limitations for solving different focus-related problems in computer vision. In order to overcome these limitations, the focus profile model is introduced as an alternative to classic concepts, such as the near and far limits of the depth-of-field. The new concepts introduced in this dissertation are exploited for solving diverse focus-related problems, such as efficient image capture, depth estimation, visual cue integration and image fusion. The results obtained through an exhaustive experimental validation demonstrate the applicability of the proposed models

Joint sparsity-driven inversion and model error correction for SAR imaging

Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this thesis is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. In this technique, phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the proposed method for various types of phase errors, as well as the improvements it provides over existing techniques for model error compensation in SAR

High resolution polarimetric imaging of biophysical objects using synthetic aperture radar.

A synthetic aperture microwave near-field system is used to image biophysical objects in order to investigate the nature of radar-target interaction. Two different imaging algorithms for focusing data collected over a two-dimensional planar aperture are investigated. The first of these is the single frequency backward propagation technique which is mathematically simple to implement and provides a high degree of resolution. Secondly, a multifrequency development of the backward propagation algorithm is presented and derived from two separate perspectives. This latter algorithm, known as the auto-focusing algorithm, requires no information about the range of the target from the aperture. Full characterisation by simulation of both algorithms is carried out and different filtering techniques are investigated. The backward propagation algorithm is applied to the polarimetric imaging of three different leafless trees and a sugar beet plant at the X-band frequency of 10GHz. The images so produced demonstrate that the backscattered signal is dependent on the orientation of individual tree elements with respect to the polarisation. Furthermore, multiple scattering terms can be identified within the structure of the tree. The auto-focusing algorithm is applied to the polarimetric imaging of two trees at 10GHz and repeat measurements are made over several months. As with the single frequency measurements, the backscattered signal is dependent on the orientation of individual tree elements relative to the polarisation. The relative contributions from the leaves and branches of the trees to the backscattered signal are assessed and found to be seasonally dependent. Measurements are also carried out to investigate the variation of backscatter from a beech tree with varying incidence angle. It is demonstrated that at small angles of incidence, the leaves are the dominant source of backscatter but at large incidence angles, the branches and trunk of the tree have the greatest contrbution

Digital Hologram Image Processing

In this thesis we discuss and examine the contributions we have made to the field of digital hologram image processing. In particular, we will deal with the processing of numerical reconstructions of real-world three-dimensional macroscopic objects recorded by in-line digital holography. Our selection of in-line digital holography over off-axis digital holography is based primarily on resolution. There is evidence that an off-axis architecture requires approximately four times the resolution to record a hologram than an in-line architecture. The high resolution of holographic film means this is acceptable in optical holography. However, in digital holography the bandwidth of the recording medium is already severely limited and if we are to extract information from reconstructions we need the highest possible resolution which, if one cannot harness the functionality of accurately reconstructing phase, is achieved through using an in-line architecture. Two of the most significant problems encountered with reconstructions of in-line digital holograms include the small depth-of-field of each reconstruction and corruptive influence of the unwanted twin-image. This small depth-of-field makes it difficult to accurately process the numerical reconstructions and it is in this shortcoming that we will make our first three contributions: focusing algorithms, background and object segmentation algorithms and algorithms to create a single image where all object regions are in focus. Using a combination of our focusing algorithms and our background segmentation algorithm, we will make our fourth contribution: a rapid twin-image reduction algorithm for in-line digital holography. We believe that our techniques would be applicable to all digital holographic objects, in particular its relevant to objects where phase unwrapping is not an option. We demonstrate the usefulness of the algorithms for a range of macroscopic objects with varying texture and contrast

Digital Hologram Image Processing

In this thesis we discuss and examine the contributions we have made to the field of digital hologram image processing. In particular, we will deal with the processing of numerical reconstructions of real-world three-dimensional macroscopic objects recorded by in-line digital holography. Our selection of in-line digital holography over off-axis digital holography is based primarily on resolution. There is evidence that an off-axis architecture requires approximately four times the resolution to record a hologram than an in-line architecture. The high resolution of holographic film means this is acceptable in optical holography. However, in digital holography the bandwidth of the recording medium is already severely limited and if we are to extract information from reconstructions we need the highest possible resolution which, if one cannot harness the functionality of accurately reconstructing phase, is achieved through using an in-line architecture. Two of the most significant problems encountered with reconstructions of in-line digital holograms include the small depth-of-field of each reconstruction and corruptive influence of the unwanted twin-image. This small depth-of-field makes it difficult to accurately process the numerical reconstructions and it is in this shortcoming that we will make our first three contributions: focusing algorithms, background and object segmentation algorithms and algorithms to create a single image where all object regions are in focus. Using a combination of our focusing algorithms and our background segmentation algorithm, we will make our fourth contribution: a rapid twin-image reduction algorithm for in-line digital holography. We believe that our techniques would be applicable to all digital holographic objects, in particular its relevant to objects where phase unwrapping is not an option. We demonstrate the usefulness of the algorithms for a range of macroscopic objects with varying texture and contrast

Autofocus and Back-Projection in Synthetic Aperture Radar Imaging.

Spotlight-mode Synthetic Aperture Radar (SAR) imaging has received considerable attention due to its ability to produce high-resolution images of scene reflectivity. One of the main challenges in successful image recovery is the problem of defocusing, which occurs due to inaccuracies in the estimated round-trip delays of the transmitted radar pulses. The problem is most widely studied for far-field imaging scenarios with a small range of look angles since the problem formulation can be significantly simplified under the assumptions of planar wavefronts and one-dimensional defocusing. In practice, however, these assumptions are frequently violated. MultiChannel Autofocus (MCA) is a subspace-based approach to the defocusing problem that was originally proposed for far-field imaging, with a small range of look angles. A key motivation behind MCA is the observation that there exists a low-return region within the recovered image, due to the weak illumination near the edges of the antenna footprint. The strength of the MCA formulation is that it can be easily extended to more realistic scenarios with polar-format data, spherical wavefronts, and arbitrary terrain, due to its flexible linear-algebraic framework. The main aim of this thesis is to devise a more broadly effective autofocus approach by adopting MCA to the aforementioned scenarios. By forming the solution space in a domain where the defocusing effect is truly one-dimensional, we show that drastically improved restorations can be obtained for applications with small to fairly wide ranges of look angles. When the terrain topography is known, we utilize the versatile backprojection-based imaging methods in the model formulations for MCA to accurately account for the underlying geometry. The proposed extended MCA shows reductions in RMSE of up to 50% when the underlying terrain is highly elevated. We also analyze the effects of the filtering step, the amount of wave curvature, the shape of the terrain, and the flight path of the radar, on the reconstructed image via backprojection. Finally, we discuss the selection of low-return constraints and the importance of using terrain elevation within MCA formulation.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135868/1/zzon_1.pd

Space/time/frequency methods in adaptive radar

Radar systems may be processed with various space, time and frequency techniques. Advanced radar systems are required to detect targets in the presence of jamming and clutter. This work studies the application of two types of radar systems. It is well known that targets moving along-track within a Synthetic Aperture Radar field of view are imaged as defocused objects. The SAR stripmap mode is tuned to stationary ground targets and the mismatch between the SAR processing parameters and the target motion parameters causes the energy to spill over to adjacent image pixels, thus hindering target feature extraction and reducing the probability of detection. The problem can be remedied by generating the image using a filter matched to the actual target motion parameters, effectively focusing the SAR image on the target. For a fixed rate of motion the target velocity can be estimated from the slope of the Doppler frequency characteristic. The problem is similar to the classical problem of estimating the instantaneous frequency of a linear FM signal (chirp). The Wigner-Ville distribution, the Gabor expansion, the Short-Time Fourier transform and the Continuous Wavelet Transform are compared with respect to their performance in noisy SAR data to estimate the instantaneous Doppler frequency of range compressed SAR data. It is shown that these methods exhibit sharp signal-to-noise threshold effects. The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. It is shown that reduced-rank methods outperform full-rank space-time adaptive processing when the space-time covariance matrix is estimated from a dataset with limited support. The utility of reduced-rank methods is demonstrated by theoretical analysis, simulations and analysis of real data. It is shown that reduced-rank processing has two effects on the performance: increased statistical stability which tends to improve performance, and introduction of a bias which lowers the signal-to-noise ratio. A method for evaluating the theoretical conditioned SNR for fixed reduced-rank transforms is also presented