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DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks

Abstract · Apr 8, 2017 10:27 ·

dslr cnn photos quality mse image artificially smartphone rendition loss cs-cv

Arxiv Abstract

  • Andrey Ignatov
  • Nikolay Kobyshev
  • Kenneth Vanhoey
  • Radu Timofte
  • Luc Van Gool

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. The first two losses are defined analytically, while the texture loss is learned using an adversarial network. We also present 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 images demonstrate the quality comparable to DSLR-taken photos, while the method itself can be applied to any type of digital cameras.

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