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publications
LoopExpose: An Unsupervised Framework for Arbitrary-Length Exposure Correction
2026 · Accepted at IEEE Transactions on Image Processing (TIP)
LoopExpose learns arbitrary-length exposure correction without paired labels through a nested correction-fusion loop. Multi-exposure fusion iteratively refines pseudo-labels, while luminance-ranking supervision preserves the relative brightness order of the input sequence.
Rethinking Exposure Correction for Spatially Non-uniform Degradation
Under Review
This work reformulates exposure correction around spatially non-uniform degradation. It predicts region-adaptive modulation signals for explicit 3D LUT transformation, supplements color fidelity in HSL space, and uses uncertainty-aware optimization to balance heterogeneous local restoration demands.
ZeroEC: A Zero-Reference Framework for Robust Exposure Correction via Consistency Learning
Under Review
ZeroEC removes dependence on curated multi-exposure data by synthesizing continuous exposure degradations online and enforcing consistent correction across them. An inverse-exposure constraint exploits complementary observable regions to improve severely degraded areas while remaining applicable to different correction backbones.
teaching
Artificial Intelligence System Experiment
Lab, School of Artificial Intelligence, Xidian University, 2024
Expected to be the Teaching Assistant of Artificial Intelligence System Experiment every spring term