In contrast to traditional image restoration methods, all-in-one image restoration techniques are gaining increased attention for their ability to restore images affected by diverse and unknown corruption types and levels. However, contemporary all-in-one image restoration methods omit task-wise difficulties and employ the same networks to reconstruct images afflicted by diverse degradations. This practice leads to an underestimation of the task correlations and suboptimal allocation of computational resources. To elucidate task-wise complexities, we introduce a novel concept positing that intricate image degradation can be represented in terms of elementary degradation. Building upon this foundation, we propose an innovative approach, termed the Unified-Width Adaptive Dynamic Network (U-WADN), consisting of two pivotal components: a Width Adaptive Backbone (WAB) and a Width Selector (WS). The WAB incorporates several nested sub-networks with varying widths, which facilitates the selection of the most apt computations tailored to each task, thereby striking a balance between accuracy and computational efficiency during runtime. For different inputs, the WS automatically selects the most appropriate sub-network width, taking into account both task-specific and sample-specific complexities. Extensive experiments across a variety of image restoration tasks demonstrate that the proposed U-WADN achieves better performance while simultaneously reducing up to 32.3\% of FLOPs and providing approximately 15.7\% real-time acceleration. The code has been made available at \url{https://github.com/xuyimin0926/U-WADN}.

翻译：与传统的图像恢复方法不同，全能图像恢复技术因其能够恢复受多种未知退化类型和程度影响的图像而日益受到关注。然而，当代的全能图像恢复方法忽略了任务难度差异，采用相同的网络来重建遭受不同退化的图像。这种做法导致对任务相关性的低估以及计算资源的次优分配。为阐明任务复杂度，我们引入了一个新概念，即复杂图像退化可以用基本退化的组合来表示。基于此，我们提出了一种创新方法，称为统一宽度自适应动态网络（U-WADN），由两个关键组件组成：宽度自适应骨干网络（WAB）和宽度选择器（WS）。WAB包含多个不同宽度的嵌套子网络，便于为每项任务选择最合适的计算量，从而在运行时实现精度与计算效率之间的平衡。针对不同输入，WS自动选择最合适的子网络宽度，同时考虑任务特定和样本特定的复杂度。在多种图像恢复任务上的大量实验表明，所提出的U-WADN在提升性能的同时，能减少高达32.3%的FLOPs，并提供约15.7%的实时加速。代码已开源在\url{https://github.com/xuyimin0926/U-WADN}。