8 citations · 11 across the 11 of their papers we have counts for
11 papers · 1 filter
RPBA-Net: An Interpretable Residual Pyramid Bilateral Affine Network for RAW-Domain ISP Enhancement
Yucheng Xin, Wu Chen, Xiang Chen +7
To address module fragmentation, uninterpretable mappings, and deployment constraints in RAW-domain demosaicing, color correction, and detail enhancement, this paper proposes RPBA-…
6thGrid-Net: Unified Remote Sensing Image Dehazing Based on Color Restoration and Edge-Preserving
Runci Bai, Kui Jiang, Xiang Chen +4
Remote sensing images are frequently degraded by adverse weather conditions, particularly clouds and haze, which severely impair downstream applications. Existing restoration metho…
Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration
Chen Wu, Ling Wang, Zhuoran Zheng +6
Ultra-High-Definition (UHD) image restoration is trapped in a scalability crisis: existing models, bound to pixel-wise operations, demand unsustainable computation. While state spa…
Derain-Agent: A Plug-and-Play Agent Framework for Rainy Image Restoration
Zhaocheng Yu, Xiang Chen, Runzhe Li +4
While deep learning has advanced single-image deraining, existing models suffer from a fundamental limitation: they employ a static inference paradigm that fails to adapt to the co…
UHD Image Deblurring via Autoregressive Flow with Ill-conditioned Constraints
Yucheng Xin, Dawei Zhao, Xiang Chen +6
Ultra-high-definition (UHD) image deblurring poses significant challenges for UHD restoration methods, which must balance fine-grained detail recovery and practical inference effic…
FoundIR-v2: Optimizing Pre-Training Data Mixtures for Image Restoration Foundation Model
Xiang Chen, Jinshan Pan, Jiangxin Dong +2
Recent studies have witnessed significant advances in image restoration foundation models driven by improvements in the scale and quality of pre-training data. In this work, we fin…
FoundIR: Unleashing Million-scale Training Data to Advance Foundation Models for Image Restoration
Hao Li, Xiang Chen, Jiangxin Dong +2
Despite the significant progress made by all-in-one models in universal image restoration, existing methods suffer from a generalization bottleneck in real-world scenarios, as they…
Towards Ultra-High-Definition Image Deraining: A Benchmark and An Efficient Method
Hongming Chen, Xiang Chen, Chen Wu +3
Despite significant progress has been made in image deraining, existing approaches are mostly carried out on low-resolution images. The effectiveness of these methods on high-resol…
Collaborative Feedback Discriminative Propagation for Video Super-Resolution
Hao Li, Xiang Chen, Jiangxin Dong +2
The key success of existing video super-resolution (VSR) methods stems mainly from exploring spatial and temporal information, which is usually achieved by a recurrent propagation…
Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining
Xiang Chen, Jinshan Pan, Jiangxin Dong
How to effectively explore multi-scale representations of rain streaks is important for image deraining. In contrast to existing Transformer-based methods that depend mostly on sin…
Towards Unified Deep Image Deraining: A Survey and A New Benchmark
Xiang Chen, Jinshan Pan, Jiangxin Dong +1
Recent years have witnessed significant advances in image deraining due to the kinds of effective image priors and deep learning models. As each deraining approach has individual s…