Adaptive Plug-and-Play Image Restoration for Diffractive Remote Sensing with a Latent Diffusion Prior

Diffractive optical elements (DOEs) provide an ultra-lightweight route to large-aperture imaging and are therefore attractive for lightweight large-aperture remote sensing. However, images directly captured by DOE-based diffraction systems often suffer from blur and low contrast caused by order aliasing, spatially varying blur, and uncertain noise levels. These degradations reduce image interpretability in remote sensing scenes, while the lack of large-scale paired diffraction datasets further limits data-driven restoration. To address this problem, we propose PLDIR, a diffraction remote sensing image restoration framework that integrates maximum a posteriori (MAP) estimation with a latent diffusion prior. PLDIR reduces manual noise-level tuning and alleviates the limitation of globally uniform denoising strength in conventional plug-and-play restoration. The framework contains three stages. Stage 1 trains a latent relationship encoder (LRE) to capture the latent relationship Hgt between clean and noisy images. Stage 2 freezes the trained LRE and learns a latent diffusion denoising model (LDDM) to predict Hprev from noisy images, providing a noise-aware prior for adaptive denoising. Stage 3 embeds the LDDM into the half-quadratic splitting (HQS) framework, where Hprev regulates both the denoising strength and the spatially adaptive penalty matrix during iteration. Experiments on synthetic diffraction remote sensing data and real captured diffraction images demonstrate that PLDIR improves restoration quality and detail preservation over representative baselines, providing an effective restoration approach for lightweight diffraction remote sensing.