Consensus Model of Families of Images using Tensor-based Fourier Analysis

A consensus model is a statistical approach that uses a family of signals or in our case, a family of images to generate a predictive model. In this thesis, we consider a family of images that are represented as tensors. In particular, our images are (2,0)-tensors. The consensus model is produced by utilizing the quantum Fourier transform of a family of images as tensors to transform images to images. We write a quantum Fourier transform in the numerical computation library for Python, known as Theano to produce the consensus spectrum. From the consensus spectrum, we produce the consensus model via the inverse quantum Fourier transform. Our method seeks to improve upon the phase reconstruction problem when transforming images to images under a 2-dimensional consensus model by considering images as (2,0)-tensors