Quantum Computing Application in Super-Resolution for Surveillance Imagery
Quantum Computing, Super-Resolution, Imagery Enhancement
Super-Resolution (SR) is a technique that has been exhaustively exploited and incorporates
strategic possibilities 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
surveillance 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.