Multi-Weather Image Restoration via Histogram-Based Transformer Feature Enhancement
Currently, the mainstream restoration tasks under adverse weather conditions have predominantly focused on single-weather scenarios. However, in reality, multiple weather conditions always coexist and their degree of mixing is usually unknown. Under such complex and diverse weather conditions, single-weather restoration models struggle to meet practical demands. This is particularly critical in fields such as autonomous driving, where there is an urgent need for a model capable of effectively handling mixed weather conditions and enhancing image quality in an automated manner. In this paper, we propose a Task Sequence Generator module that, in conjunction with the Task Intra-patch Block, effectively extracts task-specific features embedded in degraded images. The Task Intra-patch Block introduces an external learnable sequence that aids the network in capturing task-specific information. Additionally, we employ a histogram-based transformer module as the backbone of our network, enabling the capture of both global and local dynamic range features. Our proposed model achieves state-of-the-art performance on public datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingImage RestorationSimilar Papers 제목 키워드 기반
Restoring Images in Adverse Weather Conditions via Histogram Transformer
Transformer-based image restoration methods in adverse weather have achieved significant progress. Most of them use self-attention along the channel dimension or within spatially fixed-range blocks to reduce computationa…
Image RestorationMWFormer: Multi-Weather Image Restoration Using Degradation-Aware Transformers
Restoring images captured under adverse weather conditions is a fundamental task for many computer vision applications. However, most existing weather restoration approaches are only capable of handling a specific type o…
Contrastive LearningImage RestorationGridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions
Image restoration in adverse weather conditions is a difficult task in computer vision. In this paper, we propose a novel transformer-based framework called GridFormer which serves as a backbone for image restoration und…
Image RestorationRain RemovalRestoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models
Image restoration under adverse weather conditions has been of significant interest for various computer vision applications. Recent successful methods rely on the current progress in deep neural network architectural de…
DenoisingImage RestorationRaindrop RemovalRain Removal+1Beyond Degradation Conditions: All-in-One Image Restoration via HOG Transformers
All-in-one image restoration, which aims to address diverse degradations within a unified framework, is critical for practical applications. However, existing methods rely on predicting and integrating degradation condit…
AllImage Restoration