Aerial image quality enhancement via correction of spatially variant aberrations.

Aerial cameras are susceptible to optical aberrations under complex operating conditions, resulting in spatially varying image degradation. This degradation essentially stems from the point spread function (PSF) varying continuously across both spatial and temporal dimensions, exhibiting non-stationary and non-uniform characteristics. Such variability significantly increases the difficulty of correction and impairs image quality restoration. To address this challenge, we propose an image quality enhancement algorithm targeting spatially variant aberrations. First, due to the dynamic nature of the PSF, which makes it difficult to measure and model, we introduce a spatially varying PSF model guided by optical priors from Seidel aberrations. By constraining the blind estimation of PSFs using Seidel coefficients, the model explicitly characterizes their spatial variation. Then, to effectively correct the spatially varying aberrations while balancing computational efficiency and algorithmic performance, we adopt a strategy that combines patch-wise deconvolution with a Plug-and-Play (PnP) prior. Specifically, patch-wise deconvolution is used for initial correction, followed by a pretrained PnP network as a regularization term to impose global consistency across the image. Iterative refinement between deconvolution and PnP regularization further enhances image quality. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art approaches in both synthetic and real-world scenarios, reducing the required number of iterations from 8 to 3, and improving the Neural Image Assessment (NIMA) scores by 7.49% and the Hyper Image Quality Assessment (HyperIQA) scores by 14.15% for visible-light cameras, while achieving improvements of 29.58% and 17.53% respectively for infrared cameras.