25 citations · 87 across the 16 of their papers we have counts for
16 papers
Haze-Aware Attention Network for Single-Image Dehazing
Lihan Tong, Yun Liu, Weijia Li +2
Single-image dehazing is a pivotal challenge in computer vision that seeks to remove haze from images and restore clean background details. Recognizing the limitations of tradition…
Parallel Cross Strip Attention Network for Single Image Dehazing
Lihan Tong, Yun Liu, Tian Ye +3
The objective of single image dehazing is to restore hazy images and produce clear, high-quality visuals. Traditional convolutional models struggle with long-range dependencies due…
Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks
Sixiang Chen, Tian Ye, Jinbin Bai +3
In the real world, image degradations caused by rain often exhibit a combination of rain streaks and raindrops, thereby increasing the challenges of recovering the underlying clean…
Five A Network: You Only Need 9K Parameters for Underwater Image Enhancement
Jingxia Jiang, Tian Ye, Jinbin Bai +5
A lightweight underwater image enhancement network is of great significance for resource-constrained platforms, but balancing model size, computational efficiency, and enhancement…
NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer
Yun Liu, Zhongsheng Yan, Sixiang Chen +3
Nighttime image dehazing is a challenging task due to the presence of multiple types of adverse degrading effects including glow, haze, blurry, noise, color distortion, and so on.…
DEHRFormer: Real-time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes
Sixiang Chen, Tian Ye, Jun Shi +4
Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges. Recent learning-based depth estimation methods are mainly targeted at dehazing first…
RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement
Jingxia Jiang, Jinbin Bai, Yun Liu +4
Underwater images typically experience mixed degradations of brightness and structure caused by the absorption and scattering of light by suspended particles. To address this issue…
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…
Perceiving and Modeling Density is All You Need for Image Dehazing
Tian Ye, Mingchao Jiang, Yunchen Zhang +4
In the real world, the degradation of images taken under haze can be quite complex, where the spatial distribution of haze is varied from image to image. Recent methods adopt deep…
Efficient Re-parameterization Residual Attention Network For Nonhomogeneous Image Dehazing
Tian Ye, ErKang Chen, XinRui Huang +1
This paper proposes an end-to-end Efficient Re-parameterizationResidual Attention Network(ERRA-Net) to directly restore the nonhomogeneous hazy image. The contribution of this pape…