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.

本文提出了一种利用深度学习技术将手机拍摄照片转化为数码单反相机的图像质量。通过残差卷积神经网络提高了照片的颜色还原和图像锐度，使用一个复合知觉误差函数来度量图像质量，其中包括内容，颜色和纹理损失，并通过 DPED 数据集进行了定量和定性性评估，结果表明增强的图像质量可与数码单反照片相媲美，这种方法适用于任何类型的数码相机。