19 citations · 67 across the 25 of their papers we have counts for
7 papers · 1 filter
Dual-former: Hybrid Self-attention Transformer for Efficient Image Restoration
Sixiang Chen, Tian Ye, Yun Liu +1
Recently, image restoration transformers have achieved comparable performance with previous state-of-the-art CNNs. However, how to efficiently leverage such architectures remains a…
SnowFormer: Context Interaction Transformer with Scale-awareness for Single Image Desnowing
Sixiang Chen, Tian Ye, Yun Liu +1
Due to various and complicated snow degradations, single image desnowing is a challenging image restoration task. As prior arts can not handle it ideally, we propose a novel transf…
Towards Real-time High-Definition Image Snow Removal: Efficient Pyramid Network with Asymmetrical Encoder-decoder Architecture
Tian Ye, Sixiang Chen, Yun Liu +2
In winter scenes, the degradation of images taken under snow can be pretty complex, where the spatial distribution of snowy degradation is varied from image to image. Recent method…
MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing
Sixiang Chen, Tian Ye, Yun Liu +4
Snow removal causes challenges due to its characteristic of complex degradations. To this end, targeted treatment of multi-scale snow degradations is critical for the network to le…
Underwater Light Field Retention : Neural Rendering for Underwater Imaging
Tian Ye, Sixiang Chen, Yun Liu +3
Underwater Image Rendering aims to generate a true-tolife underwater image from a given clean one, which could be applied to various practical applications such as underwater image…
Towards Efficient Single Image Dehazing and Desnowing
Tian Ye, Sixiang Chen, Yun Liu +2
Removing adverse weather conditions like rain, fog, and snow from images is a challenging problem. Although the current recovery algorithms targeting a specific condition have made…
Mutual Learning for Domain Adaptation: Self-distillation Image Dehazing Network with Sample-cycle
Tian Ye, Yun Liu, Yunchen Zhang +2
Deep learning-based methods have made significant achievements for image dehazing. However, most of existing dehazing networks are concentrated on training models using simulated h…