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 image restoration 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