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.

翻译：本文提出了一种基于量子计算的算法来解决单图像超分辨率（SISR）问题。经典SISR方法中，一种广为人知的方法是依赖于对该问题进行成熟的基于图像块的稀疏建模。然而，当前该领域的现状是，深度神经网络（DNN）已展现出远优于传统方法的性能。尽管如此，量子计算预计将在不久的将来在机器学习问题中变得日益重要。因此，在本工作中，我们有幸尝试将量子计算算法应用于这一重要的图像增强问题，即SISR。在量子计算的两种范式——通用门量子计算和绝热量子计算（AQC）中，后者已成功应用于实际计算机视觉问题，其中利用量子并行性高效地解决组合优化问题。本研究展示了如何将量子SISR问题形式化为一个稀疏编码优化问题，并通过D-Wave Leap平台访问量子退火器进行求解。实验证明，所提出的基于AQC的算法在保持与经典模拟方法相当的SISR精度的同时，能够实现加速提升。