Constrained least-squares digital image restoration

The design of a digital image restoration filter must address four concerns: the completeness of the underlying imaging system model, the validity of the restoration metric used to derive the filter, the computational efficiency of the algorithm for computing the filter values and the ability to apply the filter in the spatial domain. Consistent with these four concerns, this dissertation presents a constrained least-squares (CLS) restoration filter for digital image restoration. The CLS restoration filter is based on a comprehensive, continuous-input/discrete- processing/continuous-output (c/d/c) imaging system model that accounts for acquisition blur, spatial sampling, additive noise and imperfect image reconstruction. The c/d/c model-based CLS restoration filter can be applied rigorously and is easier to compute than the corresponding c/d/c model-based Wiener restoration filter. The CLS restoration filter can be efficiently implemented in the spatial domain as a small convolution kernel. Simulated restorations are used to illustrate the CLS filter\u27s performance for a range of imaging conditions. Restoration studies based, in part, on an actual Forward Looking Infrared (FLIR) imaging system, show that the CLS restoration filter can be used for effective range reduction. The CLS restoration filter is also successfully tested on blurred and noisy radiometric images of the earth\u27s outgoing radiation field from a satellite-borne scanning radiometer used by the National Aeronautics and Space Administration (NASA) for atmospheric research

Super-Resolution of Unmanned Airborne Vehicle Images with Maximum Fidelity Stochastic Restoration

Super-resolution (SR) refers to reconstructing a single high resolution (HR) image from a set of subsampled, blurred and noisy low resolution (LR) images. One may, then, envision a scenario where a set of LR images is acquired with sensors on a moving platform like unmanned airborne vehicles (UAV). Due to the wind, the UAV may encounter altitude change or rotational effects which can distort the acquired as well as the processed images. Also, the visual quality of the SR image is affected by image acquisition degradations, the available number of the LR images and their relative positions. This dissertation seeks to develop a novel fast stochastic algorithm to reconstruct a single SR image from UAV-captured images in two steps. First, the UAV LR images are aligned using a new hybrid registration algorithm within subpixel accuracy. In the second step, the proposed approach develops a new fast stochastic minimum square constrained Wiener restoration filter for SR reconstruction and restoration using a fully detailed continuous-discrete-continuous (CDC) model. A new parameter that accounts for LR images registration and fusion errors is added to the SR CDC model in addition to a multi-response restoration and reconstruction. Finally, to assess the visual quality of the resultant images, two figures of merit are introduced: information rate and maximum realizable fidelity. Experimental results show that quantitative assessment using the proposed figures coincided with the visual qualitative assessment. We evaluated our filter against other SR techniques and its results were found to be competitive in terms of speed and visual quality

Mathematical Model Development of Super-Resolution Image Wiener Restoration

In super-resolution (SR), a set of degraded low-resolution (LR) images are used to reconstruct a higher-resolution image that suffers from acquisition degradations. One way to boost SR images visual quality is to use restoration filters to remove reconstructed images artifacts. We propose an efficient method to optimally allocate the LR pixels on the high-resolution grid and introduce a mathematical derivation of a stochastic Wiener filter. It relies on the continuous-discrete-continuous model and is constrained by the periodic and nonperiodic interrelationships between the different frequency components of the proposed SR system. We analyze an end-to-end model and formulate the Wiener filter as a function of the parameters associated with the proposed SR system such as image gathering and display response indices, system average signal-to-noise ratio, and inter-subpixel shifts between the LR images. Simulation and experimental results demonstrate that the derived Wiener filter with the optimal allocation of LR images results in sharper reconstruction. When compared with other SR techniques, our approach outperforms them in both quality and computational time

Sub-pixel techniques to improve spatial resolution

Image acquisition using a scene sampling device generally results in a loss of fidelity in the acquired image, particularly if the scene contains high frequency features. Acquired images are also degraded by the blurring effects of acquisition filtering, image reconstruction, and additive noise effects. to compensate for these degradations, a digital restoration filter that attempts to partially eliminate the blurring while avoiding amplification of the noise effects is needed. In addition, to compensate for undersampling, a subpixel technique known as microscanning is required. This dissertation provides research into the spatial resolution enhancement of digital images based on subpixel techniques that will help to minimize the impact of these degradations. Subpixel techniques investigated include microscanning and estimation of the function that measures the amount of blurring incurred during acquisition. These techniques will be used in conjunction with a constrained least squares restoration filter to achieve the best possible representation of the original scene

An interactive simulation environment for end-to-end digital imaging system design and fidelity analysis

The detailed specification, implementation, and documentation of an interactive software environment based on a continuous/discrete/continuous imaging system model is presented. The purpose of the interactive environment is to support the design and performance analysis of end-to-end digital imaging systems. Development of the environment is based on the objectives of acceptable response time, large sampling grid capability, good graphical user interface design, independence from proprietary applications and portability among UNIX workstations. While one-dimensional variations of interactive design environments have been developed by the commercial active filter design community, there is little or no evidence that the increased complexity associated with the extension to two dimensions had been satisfactorily accomplished prior to the work in this dissertation. The computer time versus computer memory trade-off is discussed as it applies in this particular context, and the results of a systematic study of representation passband limits are presented. The object of the study was to determine the representation passband parameters beyond which any aliasing contribution from frequencies beyond the representation passband is invariably negligible. Validation of the environment is documented by an exhaustive consideration of simple input scenes comprised of a uniform square on a uniform background, in which the square can be arbitrarily small and arbitrarily located within the scene. The effects of sampling and the dependence of those effects on sample-scene phase are illustrated in 1-D, used as a predictor for the 2-D outcome, and then illustrated in 2-D for the purpose of comparing the projected and actual results

Pixel super-resolution of time-stretch imaging by an equivalent-time sampling concept

Optical time-stretch imaging entails a stringent requirement of state-of-the-art high-speed data acquisition unit in order to preserve high image resolution at an ultrahigh frame rate-hampering the widespread application of such technology. We here propose a pixel super-resolution (pixel SR) technique tailored for time-stretch imaging that can relax the sampling rate requirement. It harnesses a concept of equivalent-time sampling, which effectively introduces sub-pixel shifts between frames. It involves no active opto-mechanical subpixel-shift control and any additional hardware. We present the system design rules and a proof-of-principle experiment which restores high-resolution images at a relaxed sampling rate of 5 GSa=s. © 2016 SPIE.published_or_final_versio

Michelson Interferometry with the Keck I Telescope

We report the first use of Michelson interferometry on the Keck I telescope for diffraction-limited imaging in the near infrared JHK and L bands. By using an aperture mask located close to the f/25 secondary, the 10 m Keck primary mirror was transformed into a separate-element, multiple aperture interferometer. This has allowed diffraction-limited imaging of a large number of bright astrophysical targets, including the geometrically complex dust envelopes around a number of evolved stars. The successful restoration of these images, with dynamic ranges in excess of 200:1, highlights the significant capabilities of sparse aperture imaging as compared with more conventional filled-pupil speckle imaging for the class of bright targets considered here. In particular the enhancement of the signal-to-noise ratio of the Fourier data, precipitated by the reduction in atmospheric noise, allows high fidelity imaging of complex sources with small numbers of short-exposure images relative to speckle. Multi-epoch measurements confirm the reliability of this imaging technique and our whole dataset provides a powerful demonstration of the capabilities of aperture masking methods when utilized with the current generation of large-aperture telescopes. The relationship between these new results and recent advances in interferometry and adaptive optics is briefly discussed.Comment: Accepted into Publications of the Astronomical Society of the Pacific. To appear in vol. 112. Paper contains 10 pages, 8 figure