8th New Trends in Image Restoration and Enhancement Workshop (NTIRE).
IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 2023.
(Oral Presentation)
Abstract
This paper proposes a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. One
of the well-known classical approaches for SISR relies on the well-established patch-wise sparse modeling of the
problem. Yet, this field's current state of affairs is that deep neural networks (DNNs) have demonstrated far superior
results than traditional approaches. Nevertheless, quantum computing is expected to become increasingly prominent for
machine learning problems soon. As a result, in this work, we take the privilege to perform an early exploration of
applying a quantum computing algorithm to this important image enhancement problem, i.e., SISR. Among the two paradigms
of quantum computing, namely universal gate quantum computing and adiabatic quantum computing (AQC), the latter has been
successfully applied to practical computer vision problems, in which quantum parallelism has been exploited to solve
combinatorial optimization efficiently. This work demonstrates formulating quantum SISR as a sparse coding optimization
problem, which is solved using quantum annealers accessed via the D-Wave Leap platform. The proposed AQC-based algorithm
is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
Paper
Quantum Annealing for Single Image Super-Resolution
Han Yao Choong, Suryansh Kumar†, Luc Van Gool.
8th NTIRE Workshop and Challenges IEEE/CVF CVPR 2023, Vancouver, Canada.
