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Gaussian Process Modeling for Upsampling Algorithms With Applications in Computer Vision and Computational Fluid Dynamics


Across a variety of fields, interpolation algorithms have been used to upsample low

resolution or coarse data fields. In this work, novel Gaussian Process based methods

are employed to solve a variety of upsampling problems. Specifically three

applications are explored: coarse data prolongation in Adaptive Mesh Refinement

(AMR) in the field of Computational Fluid Dynamics, accurate document image

upsampling to enhance Optical Character Recognition (OCR) accuracy, and fast

and accurate Single Image Super Resolution (SISR). For AMR, a new, efficient,

and “3rd order accurate” algorithm called GP-AMR is presented. Next, a novel,

non-zero mean, windowed GP model is generated to upsample low resolution document

images to generate a higher OCR accuracy, when compared to the industry

standard. Finally, a hybrid GP convolutional neural network algorithm is used to

generate a computationally efficient and high quality SISR model.

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