11 citations · 23 across the 7 of their papers we have counts for
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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…