Yuhan Chen

MIND achieves a breakthrough in medical image fusion by integrating intent-driven diagnostics, resulting in significantly improved segmentation accuracy for brain tumors.

Achieving coherent 3D scene synthesis from imperfect 2D anchors could redefine the standards for visual fidelity in urban modeling.

AIGS-Net achieves superior low-light image enhancement with only 40 learnable parameters, redefining the trade-off between quality and efficiency in image processing.

GLFS achieves superior low-light image enhancement by leveraging physical priors, outperforming existing methods in both illumination correction and detail preservation.

Fi-Gaussian recovers fine details in dehazed images by decoupling low and high-frequency information, outperforming traditional methods that struggle with detail preservation.

Achieving high-fidelity low-light image enhancement without the common pitfalls of color distortion and structural artifacts could redefine standards in vision tasks.

Achieving state-of-the-art dehazing performance with a zero-shot approach, this method leverages 2D Gaussian Splatting to redefine how we model hazy images.

Out-of-domain self-supervised pretraining on brain MRIs beats in-domain supervised learning when generalizing to real-world clinical data.

Achieve superior low-light image enhancement by decoupling illumination from signal priors, guiding a dual-stream transformer to iteratively enhance images while preserving fine details.

Stop blindly rewriting content: AgentGEO diagnoses *why* documents fail to be cited in AI responses, leading to a 40% boost in citations with minimal content changes.