14,299 research outputs found
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
The detection performance of small objects in remote sensing images is not
satisfactory compared to large objects, especially in low-resolution and noisy
images. A generative adversarial network (GAN)-based model called enhanced
super-resolution GAN (ESRGAN) shows remarkable image enhancement performance,
but reconstructed images miss high-frequency edge information. Therefore,
object detection performance degrades for small objects on recovered noisy and
low-resolution remote sensing images. Inspired by the success of edge enhanced
GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN
(EESRGAN) to improve the image quality of remote sensing images and use
different detector networks in an end-to-end manner where detector loss is
backpropagated into the EESRGAN to improve the detection performance. We
propose an architecture with three components: ESRGAN, Edge Enhancement Network
(EEN), and Detection network. We use residual-in-residual dense blocks (RRDB)
for both the ESRGAN and EEN, and for the detector network, we use the faster
region-based convolutional network (FRCNN) (two-stage detector) and single-shot
multi-box detector (SSD) (one stage detector). Extensive experiments on a
public (car overhead with context) and a self-assembled (oil and gas storage
tank) satellite dataset show superior performance of our method compared to the
standalone state-of-the-art object detectors.Comment: This paper contains 27 pages and accepted for publication in MDPI
remote sensing journal. GitHub Repository:
https://github.com/Jakaria08/EESRGAN (Implementation
Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting
This paper proposes a weakly- and self-supervised deep convolutional neural
network (WSSDCNN) for content-aware image retargeting. Our network takes a
source image and a target aspect ratio, and then directly outputs a retargeted
image. Retargeting is performed through a shift map, which is a pixel-wise
mapping from the source to the target grid. Our method implicitly learns an
attention map, which leads to a content-aware shift map for image retargeting.
As a result, discriminative parts in an image are preserved, while background
regions are adjusted seamlessly. In the training phase, pairs of an image and
its image-level annotation are used to compute content and structure losses. We
demonstrate the effectiveness of our proposed method for a retargeting
application with insightful analyses.Comment: 10 pages, 11 figures. To appear in ICCV 2017, Spotlight Presentatio
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
Context-aware Synthesis for Video Frame Interpolation
Video frame interpolation algorithms typically estimate optical flow or its
variations and then use it to guide the synthesis of an intermediate frame
between two consecutive original frames. To handle challenges like occlusion,
bidirectional flow between the two input frames is often estimated and used to
warp and blend the input frames. However, how to effectively blend the two
warped frames still remains a challenging problem. This paper presents a
context-aware synthesis approach that warps not only the input frames but also
their pixel-wise contextual information and uses them to interpolate a
high-quality intermediate frame. Specifically, we first use a pre-trained
neural network to extract per-pixel contextual information for input frames. We
then employ a state-of-the-art optical flow algorithm to estimate bidirectional
flow between them and pre-warp both input frames and their context maps.
Finally, unlike common approaches that blend the pre-warped frames, our method
feeds them and their context maps to a video frame synthesis neural network to
produce the interpolated frame in a context-aware fashion. Our neural network
is fully convolutional and is trained end to end. Our experiments show that our
method can handle challenging scenarios such as occlusion and large motion and
outperforms representative state-of-the-art approaches.Comment: CVPR 2018, http://graphics.cs.pdx.edu/project/ctxsy
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