4 citations · 4 across the 7 of their papers we have counts for
7 papers
DCA-LUT: Deep Chromatic Alignment with 5D LUT for Purple Fringing Removal
Jialang Lu, Shuning Sun, Pu Wang +6
Purple fringing, a persistent artifact caused by Longitudinal Chromatic Aberration (LCA) in camera lenses, has long degraded the clarity and realism of digital imaging. Traditional…
CAST-LUT: Tokenizer-Guided HSV Look-Up Tables for Purple Flare Removal
Pu Wang, Shuning Sun, Jialang Lu +7
Purple flare, a diffuse chromatic aberration artifact commonly found around highlight areas, severely degrades the tone transition and color of the image. Existing traditional meth…
DeLiVR: Differential Spatiotemporal Lie Bias for Efficient Video Deraining
Shuning Sun, Jialang Lu, Xiang Chen +5
Videos captured in the wild often suffer from rain streaks, blur, and noise. In addition, even slight changes in camera pose can amplify cross-frame mismatches and temporal artifac…
Latent label distribution grid representation for modeling uncertainty
ShuNing Sun, YinSong Xiong, Yu Zhang +1
Although \textbf{L}abel \textbf{D}istribution \textbf{L}earning (LDL) has promising representation capabilities for characterizing the polysemy of an instance, the complexity and h…
UniFlowRestore: A General Video Restoration Framework via Flow Matching and Prompt Guidance
Shuning Sun, Yu Zhang, Chen Wu +4
Video imaging is often affected by complex degradations such as blur, noise, and compression artifacts. Traditional restoration methods follow a "single-task single-model" paradigm…
Complex Mixer for MedMNIST Classification Decathlon
Zhuoran Zheng, Xiuyi Jia
With the development of the medical image field, researchers seek to develop a class of datasets to block the need for medical knowledge, such as \text{MedMNIST} (v2). MedMNIST (v2…
Improved Naive Bayes with Mislabeled Data
Qianhan Zeng, Yingqiu Zhu, Xuening Zhu +5
Labeling mistakes are frequently encountered in real-world applications. If not treated well, the labeling mistakes can deteriorate the classification performances of a model serio…