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IR-Flow: Bridging Discriminative and Generative Image Restoration via Rectified Flow
Zihao Fan, Xin Lu, Jie Xiao +3
In image restoration, single-step discriminative mappings often lack fine details via expectation learning, whereas generative paradigms suffer from inefficient multi-step sampling…
FinPercep-RM: A Fine-grained Reward Model and Co-evolutionary Curriculum for RL-based Real-world Super-Resolution
Yidi Liu, Zihao Fan, Jie Huang +6
Reinforcement Learning with Human Feedback (RLHF) has proven effective in image generation field guided by reward models to align human preferences. Motivated by this, adapting RLH…
Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement
Yidi Liu, Xueyang Fu, Jie Huang +5
Ultra-High Definition (UHD) image restoration faces a trade-off between computational efficiency and high-frequency detail retention. While Variational Autoencoders (VAEs) improve…
Elucidating and Endowing the Diffusion Training Paradigm for General Image Restoration
Xin Lu, Xueyang Fu, Jie Xiao +3
While diffusion models demonstrate strong generative capabilities in image restoration (IR) tasks, their complex architectures and iterative processes limit their practical applica…
CCM: Adding Conditional Controls to Text-to-Image Consistency Models
Jie Xiao, Kai Zhu, Han Zhang +5
Consistency Models (CMs) have showed a promise in creating visual content efficiently and with high quality. However, the way to add new conditional controls to the pretrained CMs…