Despite the significant progress made by deep models in various image restoration tasks, existing image restoration networks still face challenges in terms of task generality. An intuitive manifestation is that networks which excel in certain tasks often fail to deliver satisfactory results in others. To illustrate this point, we select five representative networks and conduct a comparative study on five classic image restoration tasks. First, we provide a detailed explanation of the characteristics of different image restoration tasks and backbone networks. Following this, we present the benchmark results and analyze the reasons behind the performance disparity of different models across various tasks. Drawing from this comparative study, we propose that a general image restoration backbone network needs to meet the functional requirements of diverse tasks. Based on this principle, we design a new general image restoration backbone network, X-Restormer. Extensive experiments demonstrate that X-Restormer possesses good task generality and achieves state-of-the-art performance across a variety of tasks.

翻译：尽管深度模型在各种图像修复任务中取得了显著进展，但现有图像修复网络在任务通用性方面仍面临挑战。一个直观的表现是，在某些任务中表现优异的网络往往无法在其他任务中取得令人满意的结果。为说明这一点，我们选取了五个代表性网络，在五项经典图像修复任务上进行了比较研究。首先，我们详细阐述了不同图像修复任务和骨干网络的特征。随后，我们给出了基准测试结果，并分析了不同模型在不同任务上性能差异的原因。通过这项比较研究，我们提出通用图像修复骨干网络需要满足多样化任务的功能需求。基于这一原则，我们设计了一个新的通用图像修复骨干网络X-Restormer。大量实验表明，X-Restormer具有良好的任务通用性，并在多种任务上达到了最先进的性能。