1 citations · 1 across the 10 of their papers we have counts for
10 papers
OmniVR: Audio-Video Conditional Generation for Archival Footage Restoration
Xin Lu, Zihao Fan, Mingchen Zhong +4
Archival footage often suffers from coupled visual and acoustic degradations, yet most restoration systems process the two modalities separately. To address this problem, we presen…
Event-Illumination Collaborative Low-light Image Enhancement with a High-resolution Real-world Dataset
Senyan Xu, Zhijing Sun, Kean Liu +5
Event-based low-light image enhancement (LIE) methods mainly focus on incorporating high dynamic range (HDR) information from events while overlooking the essential global illumina…
IR-Flow: Bridging Discriminative and Generative Image Restoration via Rectified Flow
Zihao Fan, Xin Lu, Jie Xiao +3
In image restoration, single-step discriminative mappings often lack fine details via expectation learning, whereas generative paradigms suffer from inefficient multi-step sampling…
LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
Xiang Chen, Hao Li, Jiangxin Dong +54
This paper presents a review for the LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aimed to advance research on real-world all-in-one image restoration…
Bird-SR: Bidirectional Reward-Guided Diffusion for Real-World Image Super-Resolution
Zihao Fan, Xin Lu, Yidi Liu +4
Powered by multimodal text-to-image priors, diffusion-based super-resolution excels at synthesizing intricate details; however, models trained on synthetic low-resolution (LR) and…
CompEvent: Complex-valued Event-RGB Fusion for Low-light Video Enhancement and Deblurring
Mingchen Zhong, Xin Lu, Dong Li +4
Low-light video deblurring poses significant challenges in applications like nighttime surveillance and autonomous driving due to dim lighting and long exposures. While event camer…
VisionSelector: End-to-End Learnable Visual Token Compression for Efficient Multimodal LLMs
Jiaying Zhu, Yurui Zhu, Xin Lu +5
Multimodal Large Language Models (MLLMs) encounter significant computational and memory bottlenecks from the massive number of visual tokens generated by high-resolution images or…
Elucidating and Endowing the Diffusion Training Paradigm for General Image Restoration
Xin Lu, Xueyang Fu, Jie Xiao +3
While diffusion models demonstrate strong generative capabilities in image restoration (IR) tasks, their complex architectures and iterative processes limit their practical applica…
NTIRE 2025 Image Shadow Removal Challenge Report
Florin-Alexandru Vasluianu, Tim Seizinger, Zhuyun Zhou +79
This work examines the findings of the NTIRE 2025 Shadow Removal Challenge. A total of 306 participants have registered, with 17 teams successfully submitting their solutions durin…
Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning
Kunyu Wang, Xueyang Fu, Xin Lu +4
Continual test-time adaptive object detection (CTTA-OD) aims to online adapt a source pre-trained detector to ever-changing environments during inference under continuous domain sh…