Consist-Retinex achieves one-step Retinex enhancement via a Retinex Transformer decomposition network and conditional consistency models trained with noise-emphasized dual objectives that align trajectory consistency to ground-truth components.
Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in Neural Infor- mation Processing Systems, 30, 2017
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Consist-Retinex: One-Step Noise-Emphasized Consistency Training Accelerates High-Quality Retinex Enhancement
Consist-Retinex achieves one-step Retinex enhancement via a Retinex Transformer decomposition network and conditional consistency models trained with noise-emphasized dual objectives that align trajectory consistency to ground-truth components.