DiSI disentangles stochastic interpolants into separate generation and regression paths, allowing controllable transitions between regression and generative image restoration with a unified few-step sampler.
In: Proceedings of the IEEE/CVF international conference on computer vision
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RL-AWB uses reinforcement learning to optimize parameters of a statistical white-balance estimator for nighttime scenes and reports better generalization on a new multi-sensor dataset.
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Disentangling Generation and Regression in Stochastic Interpolants for Controllable Image Restoration
DiSI disentangles stochastic interpolants into separate generation and regression paths, allowing controllable transitions between regression and generative image restoration with a unified few-step sampler.
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RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes
RL-AWB uses reinforcement learning to optimize parameters of a statistical white-balance estimator for nighttime scenes and reports better generalization on a new multi-sensor dataset.