Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution
This work addresses the need for compact and low-cost imaging systems by providing a universal and flexible aberration correction method, though it appears incremental as it builds on existing deep learning techniques for optical reconstruction.
The paper tackles the problem of correcting optical aberrations in low-end cameras by proposing a PSF-aware plug-and-play deep network that uses lens-specific deep priors, achieving high reconstruction performance with reduced time and memory consumption compared to patch-wise optimization or training multiple local networks.
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.