most citedSCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing

4 citations · 14 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV20241 cited

Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors

Aiping Zhang, Zongsheng Yue, Renjing Pei +2

Diffusion-based image super-resolution (SR) methods have achieved remarkable success by leveraging large pre-trained text-to-image diffusion models as priors. However, these method…

cs.CV20243 cited

Real-Time Multi-Scene Visibility Enhancement for Promoting Navigational Safety of Vessels Under Complex Weather Conditions

Ryan Wen Liu, Yuxu Lu, Yuan Gao +4

The visible-light camera, which is capable of environment perception and navigation assistance, has emerged as an essential imaging sensor for marine surface vessels in intelligent…

cs.CV20243 cited

A Hybrid Transformer-Mamba Network for Single Image Deraining

Shangquan Sun, Wenqi Ren, Juxiang Zhou +3

Existing deraining Transformers employ self-attention mechanisms with fixed-range windows or along channel dimensions, limiting the exploitation of non-local receptive fields. In r…

cs.CV2024

PAD: Patch-Agnostic Defense against Adversarial Patch Attacks

Lihua Jing, Rui Wang, Wenqi Ren +2

Adversarial patch attacks present a significant threat to real-world object detectors due to their practical feasibility. Existing defense methods, which rely on attack data or pri…

cs.CV20242 cited

DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement

Shangquan Sun, Wenqi Ren, Jingyang Peng +2

Many existing methods for low-light image enhancement (LLIE) based on Retinex theory ignore important factors that affect the validity of this theory in digital imaging, such as no…

cs.CV20241 cited

How Powerful Potential of Attention on Image Restoration?

Cong Wang, Jinshan Pan, Yeying Jin +5

Transformers have demonstrated their effectiveness in image restoration tasks. Existing Transformer architectures typically comprise two essential components: multi-head self-atten…