  High quality imaging usually requires bulky and expensive lenses to
compensate geometric and chromatic aberrations. This poses high constraints on
the optical hash or low cost applications. Although one can utilize algorithmic
reconstruction to remove the artifacts of low-end lenses, the degeneration from
optical aberrations is spatially varying and the computation has to trade off
efficiency for performance. For example, we need to conduct patch-wise
optimization or train a large set of local deep neural networks to achieve high
reconstruction performance across the whole image. In this paper, we propose a
PSF aware plug-and-play deep network, which takes the aberrant image and PSF
map as input and produces the latent high quality version via incorporating
lens-specific deep priors, thus leading to a universal and flexible optical
aberration correction method. Specifically, we pre-train a base model from a
set of diverse lenses and then adapt it to a given lens by quickly refining the
parameters, which largely alleviates the time and memory consumption of model
learning. The approach is of high efficiency in both training and testing
stages. Extensive results verify the promising applications of our proposed
approach for compact low-end cameras.
