55 research outputs found
Drone Shadow Tracking
Aerial videos taken by a drone not too far above the surface may contain the
drone's shadow projected on the scene. This deteriorates the aesthetic quality
of videos. With the presence of other shadows, shadow removal cannot be
directly applied, and the shadow of the drone must be tracked. Tracking a
drone's shadow in a video is, however, challenging. The varying size, shape,
change of orientation and drone altitude pose difficulties. The shadow can also
easily disappear over dark areas. However, a shadow has specific properties
that can be leveraged, besides its geometric shape. In this paper, we
incorporate knowledge of the shadow's physical properties, in the form of
shadow detection masks, into a correlation-based tracking algorithm. We capture
a test set of aerial videos taken with different settings and compare our
results to those of a state-of-the-art tracking algorithm.Comment: 5 pages, 4 figure
Deep Residual Network for Joint Demosaicing and Super-Resolution
In digital photography, two image restoration tasks have been studied
extensively and resolved independently: demosaicing and super-resolution. Both
these tasks are related to resolution limitations of the camera. Performing
super-resolution on a demosaiced images simply exacerbates the artifacts
introduced by demosaicing. In this paper, we show that such accumulation of
errors can be easily averted by jointly performing demosaicing and
super-resolution. To this end, we propose a deep residual network for learning
an end-to-end mapping between Bayer images and high-resolution images. By
training on high-quality samples, our deep residual demosaicing and
super-resolution network is able to recover high-quality super-resolved images
from low-resolution Bayer mosaics in a single step without producing the
artifacts common to such processing when the two operations are done
separately. We perform extensive experiments to show that our deep residual
network achieves demosaiced and super-resolved images that are superior to the
state-of-the-art both qualitatively and in terms of PSNR and SSIM metrics
VIDIT: Virtual Image Dataset for Illumination Transfer
Deep image relighting is gaining more interest lately, as it allows photo
enhancement through illumination-specific retouching without human effort.
Aside from aesthetic enhancement and photo montage, image relighting is
valuable for domain adaptation, whether to augment datasets for training or to
normalize input test data. Accurate relighting is, however, very challenging
for various reasons, such as the difficulty in removing and recasting shadows
and the modeling of different surfaces. We present a novel dataset, the Virtual
Image Dataset for Illumination Transfer (VIDIT), in an effort to create a
reference evaluation benchmark and to push forward the development of
illumination manipulation methods. Virtual datasets are not only an important
step towards achieving real-image performance but have also proven capable of
improving training even when real datasets are possible to acquire and
available. VIDIT contains 300 virtual scenes used for training, where every
scene is captured 40 times in total: from 8 equally-spaced azimuthal angles,
each lit with 5 different illuminants.Comment: For further information and data, see
https://github.com/majedelhelou/VIDI
Deep Residual Network for Joint Demosaicing and Super- Resolution
The two classic image restoration tasks, demosaicing and super-resolution, have traditionally always been studied indepen- dently. That is sub-optimal as sequential processing, demosaic- ing and then super-resolution, may lead to amplification of ar- tifacts. In this paper, we show that such accumulation of er- rors can be easily averted by jointly performing demosaicing and super-resolution. To this end, we propose a deep residual net- work for learning an end-to-end mapping between Bayer images and high-resolution images. Our deep residual demosaicing and super-resolution network is able to recover high-quality super- resolved images from low-resolution Bayer mosaics in a single step without producing the artifacts common to such processing when the two operations are done separately. We perform exten- sive experiments to show that our deep residual network achieves demosaiced and super-resolved images that are superior to the state-of-the-art both qualitatively and quantitatively
Mechanism of dissolution and oxidation of stibnite mediated by the coupling of iron and typical antimony oxidizing bacteria
Antimony oxidizing bacteria (SbOB) and iron oxides are the main driving factors to the weathering dissolution and oxidation of stibnite (Sb2S3) waste ore. The characteristics of the dissolution and oxidation process of stibnite in the absence of strain AO-1 and iron oxides, Pseudomonas sp. AO-1-mediated (AO-1-mediated), Fe (Fe, Fe2(SO4)3, and FeS2) -mediated, and coupled-mediated groups (Fe+AO-1, Fe2(SO4)3+AO-1, FeS2+AO-1) under various pH values were examined through sequential batch experiments. The results showed that all the AO-1-mediated, Fe-mediated and coupled-mediated can promote the dissolution and oxidation of stibnite, and the promotion effect increased with the rise of pH. The order of contribution to the dissolution of stibnite under the coupling mediation is as follows: coupling effect (42.4-78.2%) > chemical effect (19.4-56.6%) > biological effect (0.9-2.4%). In addition, the dissolution and oxidation mechanisms of stibnite were further investigated and analyzed in combination with scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). This study has important implications for elucidating the source control and geochemical behavior of antimony pollution in antimony mining areas
Drone Shadow Tracking
Aerial videos taken by a drone not too far above the surface may contain the drone's shadow projected on the scene. This deteriorates the aesthetic quality of videos. With the presence of other shadows, shadow removal cannot be directly applied, and the shadow of the drone must be tracked. Tracking a drone's shadow in a video is, however, challenging. The varying size, shape, change of orientation and drone altitude pose difficulties. The shadow can also easily disappear over dark areas. However, a shadow has specific properties that can be leveraged, besides its geometric shape. In this paper, we incorporate knowledge of the shadow's physical properties, in the form of shadow detection masks, into a correlation-based tracking algorithm. We capture a test set of aerial videos taken with different settings and compare our results to those of a state-of-the-art tracking algorithm
A comparative study on wavelets and residuals in deep super resolution
Despite the advances in single-image super resolution using deep convolutional networks, the main problem remains unsolved: recovering fine texture details. Recent works in super resolution aim at modifying the training of neural networks to enable the recovery of these details. Among the different method proposed, wavelet decomposition are used as inputs to super resolution networks to provide structural information about the image. Residual connections may also link different network layers to help propagate high frequencies. We review and compare the usage of wavelets and residuals in training super resolution neural networks. We show that residual connections are key in improving the performance of deep super resolution networks. We also show that there is no statistically significant performance difference between spatial and wavelet inputs. Finally, we propose a new super resolution architecture that saves memory costs while still using residual connections, and performing comparably to the current state of the art
HeteroNet: Heterophily-aware Representation Learning on Heterogenerous Graphs
Real-world graphs are typically complex, exhibiting heterogeneity in the
global structure, as well as strong heterophily within local neighborhoods.
While a growing body of literature has revealed the limitations of common graph
neural networks (GNNs) in handling homogeneous graphs with heterophily, little
work has been conducted on investigating the heterophily properties in the
context of heterogeneous graphs. To bridge this research gap, we identify the
heterophily in heterogeneous graphs using metapaths and propose two practical
metrics to quantitatively describe the levels of heterophily. Through in-depth
investigations on several real-world heterogeneous graphs exhibiting varying
levels of heterophily, we have observed that heterogeneous graph neural
networks (HGNNs), which inherit many mechanisms from GNNs designed for
homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily
or low level of homophily. To address the challenge, we present HeteroNet,
a heterophily-aware HGNN that incorporates both masked metapath prediction and
masked label prediction tasks to effectively and flexibly handle both
homophilic and heterophilic heterogeneous graphs. We evaluate the performance
of HeteroNet on five real-world heterogeneous graph benchmarks with varying
levels of heterophily. The results demonstrate that HeteroNet outperforms
strong baselines in the semi-supervised node classification task, providing
valuable insights into effectively handling more complex heterogeneous graphs.Comment: Preprin
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