16,404 research outputs found
Hypernetwork functional image representation
Motivated by the human way of memorizing images we introduce their functional
representation, where an image is represented by a neural network. For this
purpose, we construct a hypernetwork which takes an image and returns weights
to the target network, which maps point from the plane (representing positions
of the pixel) into its corresponding color in the image. Since the obtained
representation is continuous, one can easily inspect the image at various
resolutions and perform on it arbitrary continuous operations. Moreover, by
inspecting interpolations we show that such representation has some properties
characteristic to generative models. To evaluate the proposed mechanism
experimentally, we apply it to image super-resolution problem. Despite using a
single model for various scaling factors, we obtained results comparable to
existing super-resolution methods
Enhanced Deep Residual Networks for Single Image Super-Resolution
Recent research on super-resolution has progressed with the development of
deep convolutional neural networks (DCNN). In particular, residual learning
techniques exhibit improved performance. In this paper, we develop an enhanced
deep super-resolution network (EDSR) with performance exceeding those of
current state-of-the-art SR methods. The significant performance improvement of
our model is due to optimization by removing unnecessary modules in
conventional residual networks. The performance is further improved by
expanding the model size while we stabilize the training procedure. We also
propose a new multi-scale deep super-resolution system (MDSR) and training
method, which can reconstruct high-resolution images of different upscaling
factors in a single model. The proposed methods show superior performance over
the state-of-the-art methods on benchmark datasets and prove its excellence by
winning the NTIRE2017 Super-Resolution Challenge.Comment: To appear in CVPR 2017 workshop. Best paper award of the NTIRE2017
workshop, and the winners of the NTIRE2017 Challenge on Single Image
Super-Resolutio
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
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