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arXiv:2304.08924

Quantum Annealing for Single Image Super-Resolution

Han Yao Choong, Suryansh Kumar, Luc Van Gool

Published Jan 1, 2023• 0 citations
arXivHTMLPDF

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.

DOI
10.3929/ethz-b-000620613

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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.
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 accuracy1.
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.
Recent advancements in Single-Image Super-Resolution (SISR) using deep learning have significantly improved image restoration quality. However, the high computational cost of processing high-resolution images due to the large number of parameters in classical models, along with the scalability challenges of quantum algorithms for image processing, remains a major obstacle. In this paper, we propose the Quantum Image Enhancement Transformer for Super-Resolution (QUIET-SR), a hybrid framework that extends the Swin transformer architecture with a novel shifted quantum window attention mechanism, built upon variational quantum neural networks. QUIET-SR effectively captures complex residual mappings between low-resolution and high-resolution images, leveraging quantum attention mechanisms to enhance feature extraction and image restoration while requiring a minimal number of qubits, making it suitable for the Noisy Intermediate-Scale Quantum (NISQ) era. We evaluate our framework in MNIST (30.24 PSNR, 0.989 SSIM), FashionMNIST (29.76 PSNR, 0.976 SSIM) and the MedMNIST dataset collection, demonstrating that QUIET-SR achieves PSNR and SSIM scores comparable to state-of-the-art methods while using fewer parameters. Our efficient batching strategy directly enables massive parallelization on multiple QPU's paving the way for practical quantum-enhanced image super-resolution through coordinated QPU-GPU quantum supercomputing.
The ever-growing field of medical diagnostics is witnessing a paradigm shift with the integration of quantum computing and deep neural networks (DNN). This paper presents a novel approach that harnesses the computational prowess of quantum algorithms combined with the capabilities of DNNs. The core of this research lies in the development of a novel quantum-enhanced neural network model, for the complex task of reconstructing high-resolution medical images from limited or noisy data sets. The proposed model leverages quantum parallelism to significantly improve the training process of deep networks, while maintaining high fidelity in image reconstruction. Extensive experiments demonstrate that this integrated approach outperforms conventional methods in terms of both speed and accuracy. The quantum-DNN framework exhibits remarkable proficiency in handling large-scale medical images, showing substantial improvements in detecting fine-grained features critical for accurate diagnosis. This paper also delves into the optimization of quantum circuits and layer architectures, ensuring minimal qubit requirements while maximizing computational efficiency.
Abstract In image processing, image segmentation is the process of partitioning a digital image into multiple image segments. Among state‐of‐the‐art methods, Markov random fields can be used to model dependencies between pixels and achieve a segmentation by minimising an associated cost function. Currently, finding the optimal set of segments for a given image modelled as a Markov random fields appears to be NP‐hard. The authors aim to take advantage of the exponential scalability of quantum computing to speed up the segmentation of synthetic aperture radar images. For that purpose, the authors propose a hybrid quantum annealing classical optimisation expectation maximisation algorithm to obtain optimal sets of segments. After proposing suitable formulations, the authors discuss the performances and the scalability of authors’ approach on the D‐Wave quantum computer. The authors also propose a short study of optimal computation parameters to enlighten the limits and potential of the adiabatic quantum computation to solve large instances of combinatorial optimisation problems.
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 computational advantage at solving intractable problems over their classical counterparts, quantum computing emerges with the compelling prospect to offer exponential speedup to process computationally expensive operations, such as the ones verified in SR imaging. Envisioning the design of a quantum-ready method for near-term noisy devices and igniting Rapid and Accurate Image Super Resolution (RAISR), an implementation using Variational Quantum Eigensolver (VQE) is demonstrated. This study proposes an approach that combines the benefits of RAISR, a non hallucinating and computationally efficient method, and VQE, a hybrid classical-quantum algorithm, to conduct SR with the support of quantum computation, preserving quantitative performance in terms of Image Quality Assessment (IQA).