  Correcting the optical aberrations and the manufacturing deviations of
cameras is a challenging task. Due to the limitation on volume and the demand
for mass production, existing mobile terminals cannot rectify optical
degradation. In this work, we systematically construct the perturbed lens
system model to illustrate the relationship between the deviated system
parameters and the spatial frequency response measured from photographs. To
further address this issue, an optimization framework is proposed based on this
model to build proxy cameras from the machining samples' SFRs. Engaging with
the proxy cameras, we synthetic data pairs, which encode the optical
aberrations and the random manufacturing biases, for training the
learning-based algorithms. In correcting aberration, although promising results
have been shown recently with convolutional neural networks, they are hard to
generalize to stochastic machining biases. Therefore, we propose a dilated
Omni-dimensional dynamic convolution and implement it in post-processing to
account for the manufacturing degradation. Extensive experiments which evaluate
multiple samples of two representative devices demonstrate that the proposed
optimization framework accurately constructs the proxy camera. And the dynamic
processing model is well-adapted to manufacturing deviations of different
cameras, realizing perfect computational photography. The evaluation shows that
the proposed method bridges the gap between optical design, system machining,
and post-processing pipeline, shedding light on the joint of image signal
reception (lens and sensor) and image signal processing.
