35 research outputs found
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
Projection-based reduced order modeling of an iterative coupling scheme for thermo-poroelasticity
This paper explores an iterative coupling approach to solve
thermo-poroelasticity problems, with its application as a high-fidelity
discretization utilizing finite elements during the training of
projection-based reduced order models. One of the main challenges in addressing
coupled multi-physics problems is the complexity and computational expenses
involved. In this study, we introduce a decoupled iterative solution approach,
integrated with reduced order modeling, aimed at augmenting the efficiency of
the computational algorithm. The iterative coupling technique we employ builds
upon the established fixed-stress splitting scheme that has been extensively
investigated for Biot's poroelasticity. By leveraging solutions derived from
this coupled iterative scheme, the reduced order model employs an additional
Galerkin projection onto a reduced basis space formed by a small number of
modes obtained through proper orthogonal decomposition. The effectiveness of
the proposed algorithm is demonstrated through numerical experiments,
showcasing its computational prowess
Traffic-Aware Autonomous Driving with Differentiable Traffic Simulation
While there have been advancements in autonomous driving control and traffic
simulation, there have been little to no works exploring the unification of
both with deep learning. Works in both areas seem to focus on entirely
different exclusive problems, yet traffic and driving have inherent semantic
relations in the real world. In this paper, we present a generalizable
distillation-style method for traffic-informed imitation learning that directly
optimizes a autonomous driving policy for the overall benefit of faster traffic
flow and lower energy consumption. We capitalize on improving the arbitrarily
defined supervision of speed control in imitation learning systems, as most
driving research focus on perception and steering. Moreover, our method
addresses the lack of co-simulation between traffic and driving simulators and
lays groundwork for directly involving traffic simulation with autonomous
driving in future work. Our results show that, with information from traffic
simulation involved in supervision of imitation learning methods, an autonomous
vehicle can learn how to accelerate in a fashion that is beneficial for traffic
flow and overall energy consumption for all nearby vehicles
WGICP: Differentiable Weighted GICP-Based Lidar Odometry
We present a novel differentiable weighted generalized iterative closest
point (WGICP) method applicable to general 3D point cloud data, including that
from Lidar. Our method builds on differentiable generalized ICP (GICP), and we
propose using the differentiable K-Nearest Neighbor (KNN) algorithm to enhance
differentiability. The differentiable GICP algorithm provides the gradient of
output pose estimation with respect to each input point, which allows us to
train a neural network to predict its importance, or weight, in estimating the
correct pose. In contrast to the other ICP-based methods that use voxel-based
downsampling or matching methods to reduce the computational cost, our method
directly reduces the number of points used for GICP by only selecting those
with the highest weights and ignoring redundant ones with lower weights. We
show that our method improves both accuracy and speed of the GICP algorithm for
the KITTI dataset and can be used to develop a more robust and efficient SLAM
system.Comment: 6 page
ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution
Single image super-resolution (SISR) is a challenging ill-posed problem that
aims to up-sample a given low-resolution (LR) image to a high-resolution (HR)
counterpart. Due to the difficulty in obtaining real LR-HR training pairs,
recent approaches are trained on simulated LR images degraded by simplified
down-sampling operators, e.g., bicubic. Such an approach can be problematic in
practice because of the large gap between the synthesized and real-world LR
images. To alleviate the issue, we propose a novel Invertible scale-Conditional
Function (ICF), which can scale an input image and then restore the original
input with different scale conditions. By leveraging the proposed ICF, we
construct a novel self-supervised SISR framework (ICF-SRSR) to handle the
real-world SR task without using any paired/unpaired training data.
Furthermore, our ICF-SRSR can generate realistic and feasible LR-HR pairs,
which can make existing supervised SISR networks more robust. Extensive
experiments demonstrate the effectiveness of the proposed method in handling
SISR in a fully self-supervised manner. Our ICF-SRSR demonstrates superior
performance compared to the existing methods trained on synthetic paired images
in real-world scenarios and exhibits comparable performance compared to
state-of-the-art supervised/unsupervised methods on public benchmark datasets
Exploring the relationship between the spatial distribution of roads and universal pattern of travel-route efficiency in urban road networks
Urban road networks are well known to have universal characteristics and
scale-invariant patterns, despite the different geographical and historical
environments of cities. Previous studies on universal characteristics of the
urban road networks mostly have paid attention to their network properties but
often ignored the spatial networked structures. To fill the research gap, we
explore the underlying spatial patterns of road networks. In doing so, we
inspect the travel-route efficiency in a given road network across 70 global
cities which provides information on the usage pattern and functionality of the
road structure. The efficiency is quantified by the detour patterns of the
travel routes, estimated by the detour index (DI). The DI is a long-standing
popular measure, but its spatiality has been barely considered so far. In this
study, we probe the behavior of DI with respect to spatial variables by
scanning the network radially from a city center. Through empirical analysis,
we first discover universal properties in DI throughout most cities, which are
summarized as a constant behavior of DI regardless of the radial position from
a city center and clear collapse into a single curve for DIs for various radii
with respect to the angular distance. Especially, the latter enables us to know
the scaling factor in the length scale. We also reveal that the core-periphery
spatial structure of the roads induces the universal pattern, which is
supported by an artificial road network model. Furthermore, we visualize the
spatial DI pattern on the city map to figure out the city-specific
characteristics. The most and least efficient connections of several
representative cities show the potential for practical implications in
analyzing individual cities.Comment: 11 pages, 6 figure