Quantum computing application in super-resolution

Ystallonne Carlos da Silva Alves

Published Jul 31, 2019• 1 citation

Abstract

Super-Resolution (SR) is a technique that has been exhaustively exploited and incorporates strategic aspects to image processing. As quantum computers gradually evolve and provide unconditional proof of a computational advantage at solving intractable problems over their classical counterparts, quantum computing emerges with the compelling argument of offering exponential speed-up to process computationally expensive operations. Envisioning the design of parallel, quantum-ready algorithms for near-term noisy devices and igniting Rapid and Accurate Image Super Resolution (RAISR), an implementation applying variational quantum computation is demonstrated for enhancing degraded imagery. This study proposes an approach that combines the benefits of RAISR, a non hallucinating and computationally efficient method, and Variational Quantum Eigensolver (VQE), a hybrid classical-quantum algorithm, to conduct SR with the support of a quantum computer, while preserving quantitative performance in terms of Image Quality Assessment (IQA). It covers the generation of additional hash-based filters learned with the classical implementation of the SR technique, in order to further explore performance improvements, produce images that are significantly sharper, and induce the learning of more powerful upscaling filters with integrated enhancement effects. As a result, it extends the potential of applying RAISR to improve low quality assets generated by low cost cameras, as well as fosters the eventual implementation of robust image enhancement methods powered by the use of quantum computation.

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