  The recovery of high-quality images from images corrupted by lens flare
presents a significant challenge in low-level vision. Contemporary deep
learning methods frequently entail training a lens flare removing model from
scratch. However, these methods, despite their noticeable success, fail to
utilize the generative prior learned by pre-trained models, resulting in
unsatisfactory performance in lens flare removal. Furthermore, there are only
few works considering the physical priors relevant to flare removal. To address
these issues, we introduce Difflare, a novel approach designed for lens flare
removal. To leverage the generative prior learned by Pre-Trained Diffusion
Models (PTDM), we introduce a trainable Structural Guidance Injection Module
(SGIM) aimed at guiding the restoration process with PTDM. Towards more
efficient training, we employ Difflare in the latent space. To address
information loss resulting from latent compression and the stochastic sampling
process of PTDM, we introduce an Adaptive Feature Fusion Module (AFFM), which
incorporates the Luminance Gradient Prior (LGP) of lens flare to dynamically
regulate feature extraction. Extensive experiments demonstrate that our
proposed Difflare achieves state-of-the-art performance in real-world lens
flare removal, restoring images corrupted by flare with improved fidelity and
perceptual quality. The codes will be released soon.
