BiProLoRA: Bilevel Prompt LoRA for Real Scene Recovery

The emergence of large generative models has substantially advanced learning-based scene recovery in the synthetic domain. However, applying these models directly to real scenarios reveals sub-optimal performance stemming from the significant distribution gap, alongside poor adaptation to complex and unforeseen degradations. Consequently, it is imperative to develop a real scene adaptation strategy that yields faithful restorations with reliable generalizability. To this end, we propose $\textbf{Bi}$level $\textbf{Pro}$mpt $\textbf{LoRA}$, a novel learning paradigm designed to effectively adapt pre-trained generative models for real scene recovery. First, we introduce a self-supervised distribution-fidelity learning scheme to calibrate the autoencoding pathway under task-irrelevant real distributions, thereby recovering high-fidelity textures. Subsequently, a bilevel joint modeling via hyperparameter optimization is further established, empowering robust synthetic-to-real adaptation for both seen and unseen scenes by exploiting the complementary advantages between LoRA and Prompts to foster mutual promotion. Extensive evaluations on diverse real adverse scenarios demonstrate our method's superiority, with comprehensive algorithm analyses proving our effectiveness. The code will be public released upon the acceptance.