Quantum Annealing for Single Image Super-Resolution

Han Yao Choong1
Suryansh Kumar1†
Luc Van Gool1, 2

ETH Z├╝rich1, KU Lueven2
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.

[Paper]
[Code]
[YouTube]
[Bibtex]


Results

Lasso Regression

Classical and Quantum Annealing



Oral Presentation Talk





Authors

Han Yao Choong

Suryansh Kumar

Luc Van Gool




Copyright © 2023 Suryansh Kumar