AdaptBIR: Adaptive Blind Image Restoration with latent diffusion prior for higher fidelity
Yingqi Liu, Jingwen He, Yihao Liu, Xinqi Lin, Fanghua Yu, Jinfan Hu, Yu Qiao, Chao Dong
Abstract
This work aims to help diffusion models get their footing in the low-level vision field, solving the pain point of insufficient fidelity. Specifically, we propose an Adaptive Blind Image Restoration framework with latent diffusion prior — AdaptBIR, which can adaptively distinguish and address various ranges of degradations. First, we quantitatively categorize images through an Image Quality Assessment (IQA) method. Then, a dual-encoder degradation removal module is employed with the guidance of IQA scores to reach better information preservation. Lastly, we utilize a two-phase controller to handle the reconstruction process in an organized manner. Extensive experiments show that applying such an adaptive framework achieves better performance on both fidelity and perceptual metrics. In this way, AdaptBIR represents more than just a novel framework, it paves the way for a broader application of the diffusion model in blind image restoration tasks.