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收稿日期：2024-12-26，
修回日期：2025-02-11，
录用日期：2025-02-25，
网络出版日期：2025-02-26，
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高分辨率的视觉感知对场景的理解、分析等工作十分重要，但在真实场景下受限于拍摄条件、成像设备成本和系统光学器件、电路噪声和传感器的灵敏度等因素，图像往往伴随着各种降质因素影响，表现出模糊、低分辨率等特性。因此，挖掘和利用低分辨率图像中包含的相关信息，利用超分辨重建的方式提高成像分辨率具有十分重要的研究价值。为此，本文系统地分析了国际国内近年来在真实场景下图像超分辨重建领域的重要研究进展，包括问题构造和降质建模、超分辨领域常用数据集与评价指标、传统真实场景下超分辨重建、真实场景下基于监督学习的超分辨重建以及真实场景下基于无监督学习的超分辨重建技术等。其中，问题构造和降质建模部分讨论了降质成像过程及模型化方法。数据集与评价指标部分讨论了超分辨领域常见的合成数据集、真实场景数据集以及定量和定性评价方法。传统超分辨重建部分探讨了早期的超分辨重建方法，包括内插法和基于重建的方法等。基于监督学习的超分辨重建部分则以退化形式为区分，从退化未知与退化已知两个角度进行梳理，探讨如何利用监督信息指导模型参数学习。基于无监督学习的超分辨重建部分则从无监督角度分析讨论低分辨率图像信息的利用，以及退化模型的建模与估计。论文详细综述了上述研究的挑战性，梳理了国内外技术发展脉络和前沿动态。最后，根据上述分析展望了真实场景下图像超分辨技术的发展方向。
High-resolution visual perception plays a pivotal role in numerous computer vision tasks， including scene understanding， object recognition， and image analysis， as it enables the extraction of finer details that are critical for accurate and meaningful interpretations of visual data. Applications such as autonomous driving， medical imaging， remote sensing， and surveillance heavily rely on the clarity and richness of high-resolution images. However， in real-world scenarios， achieving high-quality image capture is often constrained by various practical factors. Limitations in shooting conditions， such as low lighting， motion， and adverse weather， can introduce noise， blurriness， and distortion to images. Furthermore， the cost and limitations of imaging equipment， including optical system components， sensor sensitivity， and circuit noise， often result in degraded image quality， characterized by low resolution and loss of detail. These challenges pose significant hurdles for downstream applications， necessitating methods that can enhance and recover the resolution and quality of degraded images. One promising solution to address these challenges is image super-resolution （SR）， a technique designed to reconstruct high-resolution （HR） images from low-resolution （LR） inputs. Super-resolution holds immense research value， as it not only enhances visual quality but also boosts the performance of various downstream tasks， particularly in resource-constrained scenarios where high-end imaging devices are unavailable. By extracting and utilizing the latent information present in LR images， SR techniques aim to bridge the gap between degraded and high-quality visuals， making it an active area of investigation within the fields of computer vision and image processing.This paper provides a comprehensive review of the recent advancements in image super-resolution reconstruction， focusing on progress made in addressing real-world challenges. It systematically explores the major research contributions both domestically and internationally over recent years， categorizing them into several key areas. These include problem formulation and degradation modeling， commonly used datasets and evaluation metrics in SR research， traditional super-resolution reconstruction methods， and modern approaches based on supervised and unsupervised learning. Through this structured analysis， the paper aims to offer valuable insights into the evolution of SR techniques， highlight persistent challenges， and provide an outlook on future development trends in this field. The first section of the paper addresses problem formulation and degradation modeling， which serve as the foundational step in SR research. Degradation modeling involves understanding the various processes that contribute to image degradation， such as noise， compression， and optical distortions. Researchers have proposed a variety of approaches to address this， ranging from controlled synthetic degradation modeling for benchmarking to advanced data-driven methods that learn degradation patterns directly from real-world datasets. The second section discusses datasets and evaluation metrics， which are critical for the development and validation of SR methods. Synthetic datasets， such as Set5， Set14， and DIV2K， are widely used for controlled experiments because they provide paired LR-HR image pairs generated using predefined degradation models. However， these datasets often fail to represent the complexities of real-world scenarios. In response， real-world datasets such as RealSR， DRealSR， and City100 are increasingly being utilized to train and evaluate SR models under practical conditions. The paper also examines commonly used evaluation metrics， including quantitative measures like peak signal-to-noise ratio （PSNR） and structural similarity index measure （SSIM）， as well as perceptual-based metrics like learned perceptual image patch similarity （LPIPS）. These metrics help assess the fidelity and visual quality of reconstructed images， although they often have limitations in capturing subjective human perception. The third section delves into traditional super-resolution reconstruction methods， which laid the foundation for modern SR research. Early methods primarily relied on interpolation techniques， such as bicubic interpolation， which are simple and computationally efficient but fail to recover high-frequency details. Reconstruction-based methods， on the other hand， use prior knowledge about image structures to restore missing details. Techniques like sparse representation and dictionary learning have shown promise in this area， but their reliance on handcrafted features and assumptions about image properties limits their performance in complex real-world scenarios. The fourth section explores supervised learning-based super-resolution reconstruction， which has gained immense popularity with the advent of deep learning. Supervised SR methods rely on paired LR-HR datasets to train neural networks that learn the mapping between low- and high-resolution images. However， supervised SR methods face challenges in handling unknown degradation. To address this， researchers have proposed degradation-aware networks and domain adaptation techniques that aim to bridge the gap between synthetic and real-world data， ensuring better generalization to practical scenarios. The fifth section discusses unsupervised learning-based super-resolution reconstruction， which is particularly valuable in scenarios where paired LR-HR data is unavailable. Unsupervised methods leverage techniques such as generative adversarial networks （GANs） and cycle-consistent loss functions to learn mappings between LR and HR domains without explicit supervision. For example， CycleGAN-based models align the distributions of LR and HR images， enabling effective SR reconstruction. Self-supervised learning approaches， which utilize auxiliary tasks or pretext objectives， have also shown promise in leveraging unpaired data for SR tasks. Finally， the paper concludes with a detailed discussion of the challenges and future directions in real-world image super-resolution research. Key challenges include addressing the diverse and unknown degradation patterns encountered in practical applications， improving the efficiency and scalability of SR models for deployment on resource-constrained devices， and enhancing the perceptual quality of reconstructed images. The integration of prior knowledge， such as scene semantics or geometric information， is identified as a promising direction for improving SR performance. Additionally， the paper highlights the potential of emerging techniques such as zero-shot learning， physics-informed models， and hybrid approaches that combine traditional and deep learning-based methods. In summary， this paper provides a thorough analysis of the state of the art in image super-resolution reconstruction， offering valuable insights into the progress， challenges， and future opportunities in the field. By bridging the gap between theory and application， the findings of this paper aim to advance the development of robust SR methods that can address the complexities of real-world scenarios.
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