Abstract
Existing methods for single image super-resolution (SR)
are typically evaluated with synthetic degradation models
such as bicubic or Gaussian downsampling. In this paper,
we investigate SR from the perspective of camera lenses,
named as CameraSR, which aims to alleviate the intrinsic
tradeoff between resolution (R) and field-of-view (V) in realistic imaging systems. Specifically, we view the R-V degradation as a latent model in the SR process and learn to reverse it with realistic low- and high-resolution image pairs.
To obtain the paired images, we propose two novel data acquisition strategies for two representative imaging systems
(i.e., DSLR and smartphone cameras), respectively. Based
on the obtained City100 dataset, we quantitatively analyze
the performance of commonly-used synthetic degradation
models, and demonstrate the superiority of CameraSR as
a practical solution to boost the performance of existing
SR methods. Moreover, CameraSR can be readily generalized to different content and devices, which serves as an
advanced digital zoom tool in realistic imaging systems.