3,592 research outputs found
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
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
A Fully Progressive Approach to Single-Image Super-Resolution
Recent deep learning approaches to single image super-resolution have
achieved impressive results in terms of traditional error measures and
perceptual quality. However, in each case it remains challenging to achieve
high quality results for large upsampling factors. To this end, we propose a
method (ProSR) that is progressive both in architecture and training: the
network upsamples an image in intermediate steps, while the learning process is
organized from easy to hard, as is done in curriculum learning. To obtain more
photorealistic results, we design a generative adversarial network (GAN), named
ProGanSR, that follows the same progressive multi-scale design principle. This
not only allows to scale well to high upsampling factors (e.g., 8x) but
constitutes a principled multi-scale approach that increases the reconstruction
quality for all upsampling factors simultaneously. In particular ProSR ranks
2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge
[34]. Compared to the top-ranking team, our model is marginally lower, but runs
5 times faster
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