Abstract
Super-resolution is aimed at reconstructing high-resolution images from low-resolution observations. State-of-the-art approaches underpinned with deep learning allow for obtaining outstanding results, generating images of high perceptual quality. However, it often remains unclear whether the reconstructed details are close to the actual ground-truth information and whether they constitute a more valuable source for image analysis algorithms. In the reported work, we address the latter problem, and we present our efforts toward learning super-resolution algorithms in a task-driven way to make them suitable for generating high-resolution images that can be exploited for automated image analysis. In the reported initial research, we propose a methodological approach for assessing the existing models that perform computer vision tasks in terms of whether they can be used for evaluating super-resolution reconstruction algorithms, as well as training them in a task-driven way. We support our analysis with experimental study and we expect it to establish a solid foundation for selecting appropriate computer vision tasks that will advance the capabilities of real-world super-resolution.
Abstract (translated)
超分辨率技术旨在从低分辨率观测中重建高质量的高分辨率图像。基于深度学习的最新方法能够生成具有极高感知质量的图像，取得了出色的效果。然而，通常不清楚这些重建细节是否接近实际的真实信息以及它们是否构成更有利于图像分析算法的数据源。在本报告的工作中，我们解决了后者的问题，并展示了我们在以任务驱动的方式学习超分辨率算法方面的努力，以便使其适合生成可用于自动化图像分析的高分辨率图像。 在初步研究中，我们提出了一种方法论，用于评估现有的执行计算机视觉任务的模型是否可以用来评价超分辨率重建算法的质量，以及训练它们进行任务导向的学习。我们的分析得到了实验研究的支持，并期望它为选择适当的计算机视觉任务奠定坚实基础，这些任务将提升现实世界中超分辨率技术的能力。
URL
https://arxiv.org/abs/2503.15474
