Transformer取代传统网络，显著提升图像超分辨率质量。
State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications
- 融合Transformer与传统网络，兼顾全局与局部信息
- 突破CNN/GAN在高频细节恢复上的局限性
- 适合关注深度学习图像重建的研究者
图像超分辨率（SR）旨在从受特定退化过程影响的低分辨率图像中恢复高分辨率图像，提升细节与视觉质量。近年来，基于Transformer的方法通过增强全局上下文建模能力，实现了超越传统卷积神经网络（CNN）和生成对抗网络（GAN）的高质量重建效果，有效克服了前代方法在感受野受限、全局信息捕捉不足及高频细节恢复困难等方面的缺陷。本文综述了基于Transformer的图像超分辨率模型的最新进展，分析了多种创新架构与技术，探讨了其在平衡全局与局部上下文方面的有效性。同时，研究揭示了当前方法尚未充分探索的空白点与未来潜在方向。文中包含多个模型与技术的可视化展示，有助于全面理解该领域的前沿趋势。本工作为深度学习领域的研究人员提供了一条清晰的技术发展路线图。
原文摘要 · Abstract (English)
Image Super-Resolution (SR) aims to recover a high-resolution image from its low-resolution counterpart, which has been affected by a specific degradation process. This is achieved by enhancing detail and visual quality. Recent advancements in transformer-based methods have remolded image super-resolution by enabling high-quality reconstructions surpassing previous deep-learning approaches like CNN and GAN-based. This effectively addresses the limitations of previous methods, such as limited receptive fields, poor global context capture, and challenges in high-frequency detail recovery. Additionally, the paper reviews recent trends and advancements in transformer-based SR models, exploring various innovative techniques and architectures that combine transformers with traditional networks to balance global and local contexts. These neoteric methods are critically analyzed, revealing promising yet unexplored gaps and potential directions for future research. Several visualizations of models and techniques are included to foster a holistic understanding of recent trends. This work seeks to offer a structured roadmap for researchers at the forefront of deep learning, specifically exploring the impact of transformers on super-resolution techniques.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。