19 citations · 26 across the 5 of their papers we have counts for
7 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…
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…
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…
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…