  The use of a single image restoration framework to achieve multi-task image
restoration has garnered significant attention from researchers. However,
several practical challenges remain, including meeting the specific and
simultaneous demands of different tasks, balancing relationships between tasks,
and effectively utilizing task correlations in model design. To address these
challenges, this paper explores a multi-expert adaptive selection mechanism. We
begin by designing a feature representation method that accounts for both the
pixel channel level and the global level, encompassing low-frequency and
high-frequency components of the image. Based on this method, we construct a
multi-expert selection and ensemble scheme. This scheme adaptively selects the
most suitable expert from the expert library according to the content of the
input image and the prompts of the current task. It not only meets the
individualized needs of different tasks but also achieves balance and
optimization across tasks. By sharing experts, our design promotes
interconnections between different tasks, thereby enhancing overall performance
and resource utilization. Additionally, the multi-expert mechanism effectively
eliminates irrelevant experts, reducing interference from them and further
improving the effectiveness and accuracy of image restoration. Experimental
results demonstrate that our proposed method is both effective and superior to
existing approaches, highlighting its potential for practical applications in
multi-task image restoration.
