Abstract
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. These findings highlight the potential of scalable variational quantum machine learning models for SISR, marking a step toward practical quantum-enhanced image super-resolution.
Abstract (translated)
最近，基于深度学习的单图像超分辨率（SISR）技术取得了显著进展，大幅提升了图像恢复质量。然而，由于经典模型中参数数量庞大而导致处理高分辨率图像时计算成本高昂，以及量子算法在图像处理中的可扩展性挑战，仍然是一个主要障碍。为此，本文提出了名为“用于超分辨率的量子图像增强变换器”（QUIET-SR），这是一个混合框架，它通过引入一种新颖的移位量子窗口注意机制来扩展Swin变压器架构，并基于变分量子神经网络构建而成。QUIET-SR能够有效捕捉低分辨率和高分辨率图像之间的复杂残差映射关系，在利用量子注意机制增强特征提取和图像恢复的同时，只需要少量的量子比特（qubits），使其适合于嘈杂中等规模量子（NISQ）时代。 我们通过在MNIST数据集（峰值信噪比30.24，结构相似性指数0.989）、FashionMNIST数据集（峰值信噪比29.76，结构相似性指数0.976）和MedMNIST数据集集合上评估我们的框架，证明了QUIET-SR在使用较少参数的情况下取得了与现有最佳方法相当的峰值信噪比（PSNR）和结构相似性指标（SSIM）。这些发现表明可扩展变分量子机器学习模型在SISR中的潜力，并标志着向实用量子增强图像超分辨率技术迈进的重要一步。
URL
https://arxiv.org/abs/2503.08759
