IA-CLAHE trains a lightweight network on a differentiable CLAHE extension to predict per-tile clip limits that drive local histograms toward a uniform distribution, delivering zero-shot gains in recognition accuracy and visual quality.
Zero-reference low-light enhancement via physical quadru- ple priors
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ZeroIDIR restores illumination-degraded images via adaptive gamma correction followed by perturbed consistency diffusion, trained solely on degraded images without references.
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IA-CLAHE: Image-Adaptive Clip Limit Estimation for CLAHE
IA-CLAHE trains a lightweight network on a differentiable CLAHE extension to predict per-tile clip limits that drive local histograms toward a uniform distribution, delivering zero-shot gains in recognition accuracy and visual quality.
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ZeroIDIR: Zero-Reference Illumination Degradation Image Restoration with Perturbed Consistency Diffusion Models
ZeroIDIR restores illumination-degraded images via adaptive gamma correction followed by perturbed consistency diffusion, trained solely on degraded images without references.