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In contrast to traditional image restoration methods, all-in-one imagerestoration techniques are gaining increased attention for their ability torestore images affected by diverse and unknown corruption types and levels.However, contemporary all-in-one image restoration methods omit task-wisedifficulties and employ the same networks to reconstruct images afflicted bydiverse degradations. This practice leads to an underestimation of the taskcorrelations and suboptimal allocation of computational resources. To elucidatetask-wise complexities, we introduce a novel concept positing that intricateimage degradation can be represented in terms of elementary degradation.Building upon this foundation, we propose an innovative approach, termed theUnified-Width Adaptive Dynamic Network (U-WADN), consisting of two pivotalcomponents: a Width Adaptive Backbone (WAB) and a Width Selector (WS). The WABincorporates several nested sub-networks with varying widths, which facilitatesthe selection of the most apt computations tailored to each task, therebystriking a balance between accuracy and computational efficiency duringruntime. For different inputs, the WS automatically selects the mostappropriate sub-network width, taking into account both task-specific andsample-specific complexities. Extensive experiments across a variety of imagerestoration tasks demonstrate that the proposed U-WADN achieves betterperformance while simultaneously reducing up to 32.3\% of FLOPs and providingapproximately 15.7\% real-time acceleration. The code has been made availableat \url{https://github.com/xuyimin0926/U-WADN}.

Unified-Width Adaptive Dynamic Network for All-In-One Image Restoration