Large-aperture, lightweight, and high-resolution imaging are hallmarks of major optical systems. To eliminate aberrations, traditional systems are often bulky and complex, whereas the small volume and light weight of diffractive lenses position them as potential substitutes. However, their inherent diffraction mechanism leads to severe dispersion, which limits their application in wide spectral bands. Addressing the dispersion issue in diffractive lenses, we propose a chromatic aberration correction algorithm based on compressed sensing. Utilizing the diffractive lens’s focusing ability at the reference wavelength and its degradation performance at other wavelengths, we employ compressed sensing to reconstruct images from incomplete image information. In this work, we design a harmonic diffractive lens with a diffractive order of $M=150$, an aperture of 40 mm, a focal length $f_0=320$ mm, a reference wavelength ${\lambda }_0=550$ nm, a wavelength range of 500–800 nm, and 7 annular zones. Through algorithmic recovery, we achieve clear imaging in the visible spectrum, with a peak signal-to-noise ratio (PSNR) of 22.85 dB, a correlation coefficient of 0.9596, and a root mean square error (RMSE) of 0.02, verifying the algorithm’s effectiveness.

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 $H_{gt}$ between clean and noisy images. Stage 2 freezes the trained LRE and learns a latent diffusion denoising model (LDDM) to predict $H_{prev}$ from noisy images, providing a noise-aware prior for adaptive denoising. Stage 3 embeds the LDDM into the half-quadratic splitting (HQS) framework, where $H_{prev}$ 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.