most citedRAUNE-Net: A Residual and Attention-Driven Underwater Image Enhancement Method

3 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Multi-Modal Prompt Learning on Blind Image Quality Assessment

Wensheng Pan, Timin Gao, Yan Zhang +10

Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Currently, leveraging seman…

cs.CV2024

Feature Denoising Diffusion Model for Blind Image Quality Assessment

Xudong Li, Jingyuan Zheng, Runze Hu +8

Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks. Currently, deep learning BIQA methods typically de…

cs.CV2024

Concealed Object Segmentation with Hierarchical Coherence Modeling

Fengyang Xiao, Pan Zhang, Chunming He +2

Concealed object segmentation (COS) is a challenging task that involves localizing and segmenting those concealed objects that are visually blended with their surrounding environme…

cs.CV2023

Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity

Xudong Li, Timin Gao, Runze Hu +9

The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically rely on feature extraction from upstream semantic backbone networks, assuming that all…

cs.CV20233 cited

RAUNE-Net: A Residual and Attention-Driven Underwater Image Enhancement Method

Wangzhen Peng, Chenghao Zhou, Runze Hu +2

Underwater image enhancement (UIE) poses challenges due to distinctive properties of the underwater environment, including low contrast, high turbidity, visual blurriness, and colo…