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

TL;DR

The paper introduces U-WADN, a dynamic network that adapts its width for all-in-one image restoration tasks, improving performance and efficiency by automatically selecting optimal sub-network widths based on task complexity.

Contribution

It proposes a novel unified adaptive network with width selection for diverse image restoration tasks, addressing task complexity and resource allocation issues.

Findings

Achieves better restoration performance across tasks.

Reduces up to 32.3% FLOPs during inference.

Provides approximately 15.7% real-time acceleration.

Abstract

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}.