1 citations · 1 across the 5 of their papers we have counts for
6 papers
MedAD-R1: Consistency-Reinforced Policy Optimization for Interpretable Medical Anomaly Detection
Haitao Zhang, Yingying Wang, Jiaxiang Wang +5
Medical Anomaly Detection (MedAD) offers a promising direction for medical image analysis with Large Multimodal Models (LMMs). However, progress is limited by fragmented datasets a…
PCMamba: Physics-Informed Cross-Modal State Space Model for Dual-Camera Compressive Hyperspectral Imaging
Ge Meng, Zhongnan Cai, Jingyan Tu +4
Panchromatic (PAN) -assisted Dual-Camera Compressive Hyperspectral Imaging (DCCHI) is a key technology in snapshot hyperspectral imaging. Existing research primarily focuses on exp…
FRN: Fractal-Based Recursive Spectral Reconstruction Network
Ge Meng, Zhongnan Cai, Ruizhe Chen +4
Generating hyperspectral images (HSIs) from RGB images through spectral reconstruction can significantly reduce the cost of HSI acquisition. In this paper, we propose a Fractal-Bas…
Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables
Zhongnan Cai, Yingying Wang, Hui Zheng +10
Recently, deep learning-based pan-sharpening algorithms have achieved notable advancements over traditional methods. However, deep learning-based methods incur substantial computat…
Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors
Yunlong Lin, Zhenqi Fu, Kairun Wen +7
Low-light image enhancement (LIE) aims at precisely and efficiently recovering an image degraded in poor illumination environments. Recent advanced LIE techniques are using deep ne…
AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement
Yunlong Lin, Tian Ye, Sixiang Chen +6
Existing low-light image enhancement (LIE) methods have achieved noteworthy success in solving synthetic distortions, yet they often fall short in practical applications. The limit…