7,359 research outputs found
Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks
Light field imaging extends the traditional photography by capturing both
spatial and angular distribution of light, which enables new capabilities,
including post-capture refocusing, post-capture aperture control, and depth
estimation from a single shot. Micro-lens array (MLA) based light field cameras
offer a cost-effective approach to capture light field. A major drawback of MLA
based light field cameras is low spatial resolution, which is due to the fact
that a single image sensor is shared to capture both spatial and angular
information. In this paper, we present a learning based light field enhancement
approach. Both spatial and angular resolution of captured light field is
enhanced using convolutional neural networks. The proposed method is tested
with real light field data captured with a Lytro light field camera, clearly
demonstrating spatial and angular resolution improvement
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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