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GLFS achieves superior low-light image enhancement by leveraging physical priors, outperforming existing methods in both illumination correction and detail preservation.
AIGS-Net achieves superior low-light image enhancement with only 40 learnable parameters, redefining the trade-off between quality and efficiency in image processing.
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.
Achieving high-fidelity low-light image enhancement without the common pitfalls of color distortion and structural artifacts could redefine standards in vision tasks.
Fi-Gaussian recovers fine details in dehazed images by decoupling low and high-frequency information, outperforming traditional methods that struggle with detail preservation.
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.
