  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.
