Thesis (Ph. D.)--University of Sussex, 1986. / Includes bibliographical references (p. 1-13).

<p dir="ltr">Motion blur arises from camera instability or swift movement of subjects within a scene. The objective of image deblurring is to eliminate these blur effects, thereby enhancing the image's quality. This task holds significant relevance, particularly in the era of smartphones and portable cameras. Yet, it remains a challenging issue, notwithstanding extensive research undertaken over many years. The fundamental concept in deblurring an image involves restoring a blurred pixel back to its initial state.</p><p dir="ltr">Deep learning (DL) algorithms, recognized for their capability to identify unique and significant features from datasets, have gained significant attention in the field of machine learning. These algorithms have been increasingly adopted in geoscience and remote sensing (RS) for analyzing large volumes of data. In these applications, low-level attributes like spectral and texture features form the foundational layer. The high-level feature representations derived from the upper layers of the network can be directly utilized in classifiers for pixel-based analysis. Thus, for enhancing the accuracy of classification using RS data, ensuring the clarity and quality of each collected data in the dataset is crucial for the effective construction of deep learning models.</p><p dir="ltr">In this thesis, we present the FFT-Cross Attention Transformer, an innovative approach amalgamating channel-focused and window-centric self-attention within a state-of-the-art(SOTA) Vision Transformer model. Augmented with a Fast Fourier Convolution Layer, this approach extends the Transformer's capability to capture intricate details in low-resolution images. Employing unified task pre-training during model development, we confirm the robustness of these enhancements through comprehensive testing, resulting in substantial performance gains. Notably, we achieve a remarkable 1dB improvement in the PSNR metric for remote sensing imagery, underscoring the transformative potential of the FFT-Cross Attention Transformer in advancing image processing and domain-specific vision tasks.</p>

Typescript (photocopy). / Digitized by Kansas Correctional Industries

It is necessary for a correla tor to be invariant to all distortions of a reference object while still being able to maintain good discrimination between similar objects. A filter must thus strike a compromise between the conflicting requirements of in-class distortion tolerance and out-of-class discrimination. A bandpass filter is therefore ideal; the high frequency response being attenuated to provide less sensitivity to in-class variations while the low frequencies are removed since they compromise the discriminatory ability of the filter. This study proposes a nonlinear bandpass pre-processing operation applied to both the input and the reference images prior to the correlation operation, i.e. in the space domain rather than to the spectrum. This operation is shown to result in greater tolerance to in-class variation while maintaining an excellent discrimination ability. Additionally, the introduction of non-linearity is shown to provide greater robustness in the filter response to noise and background clutter in the input scene. Utilizing the good performance of this new filter and in order to provide improved inter-class tolerance for a higher distortion range, the proposed nonlinear bandpass operation is incorporated in the synthesis of a modified Synthetic Discriminant Function (SDF) filter by applying the non-linearity to each of the bandpass filtered training set images comprising the SDF. This SDP filter is demonstrated to produce a sharp peak, and show excellent discrimination ability, while maintaining invariance to out-of-plane rotation over a distortion range of up to 90 degrees. The processing sequence of this filter is suitable for implementation by a hybrid digital/optical arrangement in which the input image is wavelet filtered in real-time by a digital signal processor (DSP). The filter performance is investigated with both input image and filter plane quantization to the limit of binary amplitude and phase, and hence it is suitable for implementation with current frame-rate Spatial Light Modulators (SLM) technology.

Three new reconstruction methods for quantitative phase imaging, including two interrelated two-dimensional methods, called multifilter phase imaging with partially coherent light and phase optical transfer function recovery, which lead to a third three-dimensional method, called tomographic deconvolution phase microscopy, were developed in response to a growing need among biomedical end users for solutions which can be integrated on standard microscope platforms. The performance of these new methods were evaluated using modelling and simulation as well as experimentation with known test cases. In addition to the development of new methods, existing methods for quantitative phase imaging were applied to characterize the effects of manufacturing, cleaving, and fusion splicing in large-mode-area erbium- and ytterbium-doped optical fibers.

published_or_final_version / abstract / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

No description available.

No description available.

No description available.