7,373 research outputs found
Process of image super-resolution
In this paper we explain a process of super-resolution reconstruction
allowing to increase the resolution of an image.The need for high-resolution
digital images exists in diverse domains, for example the medical and spatial
domains. The obtaining of high-resolution digital images can be made at the
time of the shooting, but it is often synonymic of important costs because of
the necessary material to avoid such costs, it is known how to use methods of
super-resolution reconstruction, consisting from one or several low resolution
images to obtain a high-resolution image. The american patent US 9208537
describes such an algorithm. A zone of one low-resolution image is isolated and
categorized according to the information contained in pixels forming the
borders of the zone. The category of it zone determines the type of
interpolation used to add pixels in aforementioned zone, to increase the
neatness of the images. It is also known how to reconstruct a low-resolution
image there high-resolution image by using a model of super-resolution
reconstruction whose learning is based on networks of neurons and on image or a
picture library. The demand of chinese patent CN 107563965 and the scientist
publication "Pixel Recursive Super Resolution", R. Dahl, M. Norouzi, J. Shlens
propose such methods. The aim of this paper is to demonstrate that it is
possible to reconstruct coherent human faces from very degraded pixelated
images with a very fast algorithm, more faster than compressed sensing (CS),
easier to compute and without deep learning, so without important technology
resources, i.e. a large database of thousands training images (see
arXiv:2003.13063).
This technological breakthrough has been patented in 2018 with the demand of
French patent FR 1855485 (https://patents.google.com/patent/FR3082980A1, see
the HAL reference https://hal.archives-ouvertes.fr/hal-01875898v1).Comment: 19 pages, 10 figure
PixColor: Pixel Recursive Colorization
We propose a novel approach to automatically produce multiple colorized
versions of a grayscale image. Our method results from the observation that the
task of automated colorization is relatively easy given a low-resolution
version of the color image. We first train a conditional PixelCNN to generate a
low resolution color for a given grayscale image. Then, given the generated
low-resolution color image and the original grayscale image as inputs, we train
a second CNN to generate a high-resolution colorization of an image. We
demonstrate that our approach produces more diverse and plausible colorizations
than existing methods, as judged by human raters in a "Visual Turing Test"
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning
Recent advances in Generative Adversarial Learning allow for new modalities
of image super-resolution by learning low to high resolution mappings. In this
paper we present our work using Generative Adversarial Networks (GANs) with
applications to overhead and satellite imagery. We have experimented with
several state-of-the-art architectures. We propose a GAN-based architecture
using densely connected convolutional neural networks (DenseNets) to be able to
super-resolve overhead imagery with a factor of up to 8x. We have also
investigated resolution limits of these networks. We report results on several
publicly available datasets, including SpaceNet data and IARPA Multi-View
Stereo Challenge, and compare performance with other state-of-the-art
architectures.Comment: 9 pages, 9 figures, WACV 2018 submissio
Deep Learning for Single Image Super-Resolution: A Brief Review
Single image super-resolution (SISR) is a notoriously challenging ill-posed
problem, which aims to obtain a high-resolution (HR) output from one of its
low-resolution (LR) versions. To solve the SISR problem, recently powerful deep
learning algorithms have been employed and achieved the state-of-the-art
performance. In this survey, we review representative deep learning-based SISR
methods, and group them into two categories according to their major
contributions to two essential aspects of SISR: the exploration of efficient
neural network architectures for SISR, and the development of effective
optimization objectives for deep SISR learning. For each category, a baseline
is firstly established and several critical limitations of the baseline are
summarized. Then representative works on overcoming these limitations are
presented based on their original contents as well as our critical
understandings and analyses, and relevant comparisons are conducted from a
variety of perspectives. Finally we conclude this review with some vital
current challenges and future trends in SISR leveraging deep learning
algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM
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