DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
The difficulty of obtaining paired data remains a major bottleneck for learning image restoration and enhancement models for real-world applications. Current strategies aim to synthesize realistic training data by modeling noise and degradations that appear in real-world settings. We propose DeFlow, a method for learning stochastic image degradations from unpaired …