| 内容提要: |
Despite a rapid rise in the quality of built-in smartphone cameras,
their physical limitations — small sensor size, compact
lenses and the lack of specific hardware, — impede them to
achieve the quality results of DSLR cameras. In this work we
present an end-to-end deep learning approach that bridges this
gap by translating ordinary photos into DSLR-quality images.
We propose learning the translation function using a residual
convolutional neural network that improves both color rendition
and image sharpness. Since the standard mean squared loss is
not well suited for measuring perceptual image quality, we introduce
a composite perceptual error function that combines content,
color and texture losses. The first two losses are defined
analytically, while the texture loss is learned in an adversarial
fashion. We also present DPED, a large-scale dataset that consists
of real photos captured from three different phones and one
high-end reflex camera. Our quantitative and qualitative assessments
reveal that the enhanced image quality is comparable to
that of DSLR-taken photos, while the methodology is generalized
to any type of digital camera. |