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-produced 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.  Read More