All-in-One Transformer for Image Restoration Under Adverse Weather Degradations

Severe weather restoration models often face the simultaneous interaction of multiple degradations in real-world scenarios. Existing approaches typically handle single or composite degradations based on scene descriptors derived from text or image embeddings. However, due to the varying proportions of different degradations within an image, these scene descriptors may not accurately differentiate between degradations, leading to suboptimal restoration in practical applications. To address this issue, we propose a novel Transformer-based restoration framework, AllRestorer, for dealing with four physical severe weather impairments: low-light, haze, rain, and snow. In AllRestorer, we enable the model to adaptively consider all weather impairments, thereby avoiding errors from scene descriptor misdirection. Specifically, we introduce the All-in-One Transformer Block (AiOTB), the core innovation of which is the ability to adaptively handle multiple degradations in a single image, beyond the limitation of existing Transformers that can only handle one type of degradation at a time. To accurately address different variations potentially present within the same type of degradation and minimize ambiguity, AiOTB utilizes a Composite Scene Embedding consisting of both image and text embeddings to define the degradation. Moreover, AiOTB includes an adaptive weight for each degradation, allowing for precise control of the restoration intensity. By leveraging AiOTB, AllRestorer avoids misdirection caused by inaccurate scene descriptors, achieving a 5.00 dB increase in PSNR compared to the baseline on the CDD-11 dataset.