Date of Award


Degree Name

Doctor of Philosophy


Electrical and Computer Engineering

First Advisor

Dr. Ikhlas Abdel-Qader

Second Advisor

Dr. Bradley J. Bazuin

Third Advisor

Dr. Valerian Kwigizile


Signal processing, image processing, computer vision, image restoration, object tracking, atmospheric turbulence


The removal of atmospheric turbulence (AT) distortion in long range imaging is one of the most challenging areas of research in imaging processing with an immediate need for solutions in several applications such as in military and transportation systems. AT exacerbates distortion due to non-linear geometric blur and scintillations in long-distance images and videos, severely reducing image quality and information interpretation. AT negatively impacts both human and computer vision systems, compromising visibility essential for accurate object identification and tracking.

In this dissertation, a novel sparse analysis framework is developed to address efficient AT blur and scintillation removal in video. Operating under the premise that distortion-free images should be sparse in a transform domain, the application of the dual-tree complex wavelet transform is utilized on frame bursts, allowing for a new near shift-invariant complex transform space that results in higher sparsity, higher object tracking accuracy, and better resilience against camera shake, geometric distortion, and imperfect frame registration encountered in real-world AT-distorted sequences.

Using this new complex transform space the novel Frame-Burst Coefficient Shimmer Thresholding (FBST) algorithm is developed. FBST considers the complex coefficient shimmer across multiple frames to address threshold selection and moving object blur, issues still present in other methods which utilize techniques such as averaging and empirical threshold selection. In fact, by evaluating video sequences of moving vehicles with visible license plates, we show FBST produces up to an 85% sparse reconstruction with superior visual results compared to weighted and simple thresholding approaches while preserving object motion, reducing AT distortion, and enhancing object contrast and visibility.

Moreover, compressed sensing (CS) methods to sparse AT distortion removal are also investigated through direct CS sampling of the coefficients of the complex wavelet transform, allowing us to sparsely sample and reconstruct a restored output image. This essentially develops a means to sample in real-time, store the reduced size dataset, and reconstruct a de-blurred output in the cloud for low-power portable systems. Results using AT-distorted traffic sequences indicate that CS provides up to a 75% sparse reconstruction, contrast enhancement, and de-blurring, while using only one-fifth the number of coefficients for sampling.

Overall, this dissertation presents a sparse analysis framework that is well suited to provide robust AT distortion removal that does not blur moving objects and provides a highly sparse representation with reduced storage requirements for distributed-processing and low-power monitoring systems.

Access Setting

Dissertation-Open Access