70 citations · 194 across the 14 of their papers we have counts for
15 papers · 1 filter
UDPNet: Unleashing Depth-based Priors for Robust Image Dehazing
Zengyuan Zuo, Junjun Jiang, Gang Wu +1
Image dehazing has witnessed significant advancements with the development of deep learning models. However, most existing methods focus solely on single-modal RGB features, neglec…
Boosting All-in-One Image Restoration via Self-Improved Privilege Learning
Gang Wu, Junjun Jiang, Kui Jiang +1
Unified image restoration models for diverse and mixed degradations often suffer from unstable optimization dynamics and inter-task conflicts. This paper introduces Self-Improved P…
Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration
Gang Wu, Junjun Jiang, Kui Jiang +2
All-in-One Image Restoration (AiOIR), which addresses diverse degradation types with a unified model, presents significant challenges in designing task-aware prompts that effective…
DSwinIR: Rethinking Window-based Attention for Image Restoration
Gang Wu, Junjun Jiang, Kui Jiang +2
Image restoration has witnessed significant advancements with the development of deep learning models. Transformer-based models, particularly those using window-based self-attentio…
Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training
Yuanqi Yao, Gang Wu, Kui Jiang +4
Learning a self-supervised Monocular Depth Estimation (MDE) model with great generalization remains significantly challenging. Despite the success of adversarial augmentation in th…
A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends
Junjun Jiang, Zengyuan Zuo, Gang Wu +2
Image restoration (IR) seeks to recover high-quality images from degraded observations caused by a wide range of factors, including noise, blur, compression, and adverse weather. W…
Hybrid Cost Volume for Memory-Efficient Optical Flow
Yang Zhao, Gangwei Xu, Gang Wu
Current state-of-the-art flow methods are mostly based on dense all-pairs cost volumes. However, as image resolution increases, the computational and spatial complexity of construc…
Exploiting Self-Supervised Constraints in Image Super-Resolution
Gang Wu, Junjun Jiang, Kui Jiang +1
Recent advances in self-supervised learning, predominantly studied in high-level visual tasks, have been explored in low-level image processing. This paper introduces a novel self-…
Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach
Gang Wu, Junjun Jiang, Junpeng Jiang +1
Recent progress in single-image super-resolution (SISR) has achieved remarkable performance, yet the computational costs of these methods remain a challenge for deployment on resou…
Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration
Gang Wu, Junjun Jiang, Kui Jiang +1
Contrastive learning has emerged as a prevailing paradigm for high-level vision tasks, which, by introducing properly negative samples, has also been exploited for low-level vision…
Fully Convolutional Network for Lightweight Image Super-Resolution
Gang Wu, Junjun Jiang, Kui Jiang +1
Deep models have achieved significant process on single image super-resolution (SISR) tasks, in particular large models with large kernel ( or more). However, the heavy c…
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
Lingdong Kong, Yaru Niu, Shaoyuan Xie +39
Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical a…
Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution
Gang Wu, Junjun Jiang, Yuanchao Bai +1
In recent years, the use of large convolutional kernels has become popular in designing convolutional neural networks due to their ability to capture long-range dependencies and pr…
GLaMa: Joint Spatial and Frequency Loss for General Image Inpainting
Zeyu Lu, Junjun Jiang, Junqin Huang +2
The purpose of image inpainting is to recover scratches and damaged areas using context information from remaining parts. In recent years, thanks to the resurgence of convolutional…
A Practical Contrastive Learning Framework for Single-Image Super-Resolution
Gang Wu, Junjun Jiang, Xianming Liu
Contrastive learning has achieved remarkable success on various high-level tasks, but there are fewer contrastive learning-based methods proposed for low-level tasks. It is challen…