134 research outputs found
WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond
Underwater images suffer from light refraction and absorption, which impairs
visibility and interferes the subsequent applications. Existing underwater
image enhancement methods mainly focus on image quality improvement, ignoring
the effect on practice. To balance the visual quality and application, we
propose a heuristic normalizing flow for detection-driven underwater image
enhancement, dubbed WaterFlow. Specifically, we first develop an invertible
mapping to achieve the translation between the degraded image and its clear
counterpart. Considering the differentiability and interpretability, we
incorporate the heuristic prior into the data-driven mapping procedure, where
the ambient light and medium transmission coefficient benefit credible
generation. Furthermore, we introduce a detection perception module to transmit
the implicit semantic guidance into the enhancement procedure, where the
enhanced images hold more detection-favorable features and are able to promote
the detection performance. Extensive experiments prove the superiority of our
WaterFlow, against state-of-the-art methods quantitatively and qualitatively.Comment: 10 pages, 13 figure
Breaking Modality Disparity: Harmonized Representation for Infrared and Visible Image Registration
Since the differences in viewing range, resolution and relative position, the
multi-modality sensing module composed of infrared and visible cameras needs to
be registered so as to have more accurate scene perception. In practice, manual
calibration-based registration is the most widely used process, and it is
regularly calibrated to maintain accuracy, which is time-consuming and
labor-intensive. To cope with these problems, we propose a scene-adaptive
infrared and visible image registration. Specifically, in regard of the
discrepancy between multi-modality images, an invertible translation process is
developed to establish a modality-invariant domain, which comprehensively
embraces the feature intensity and distribution of both infrared and visible
modalities. We employ homography to simulate the deformation between different
planes and develop a hierarchical framework to rectify the deformation inferred
from the proposed latent representation in a coarse-to-fine manner. For that,
the advanced perception ability coupled with the residual estimation conducive
to the regression of sparse offsets, and the alternate correlation search
facilitates a more accurate correspondence matching. Moreover, we propose the
first ground truth available misaligned infrared and visible image dataset,
involving three synthetic sets and one real-world set. Extensive experiments
validate the effectiveness of the proposed method against the
state-of-the-arts, advancing the subsequent applications.Comment: 10 pages, 11 figure
Contrastive Learning Based Recursive Dynamic Multi-Scale Network for Image Deraining
Rain streaks significantly decrease the visibility of captured images and are
also a stumbling block that restricts the performance of subsequent computer
vision applications. The existing deep learning-based image deraining methods
employ manually crafted networks and learn a straightforward projection from
rainy images to clear images. In pursuit of better deraining performance, they
focus on elaborating a more complicated architecture rather than exploiting the
intrinsic properties of the positive and negative information. In this paper,
we propose a contrastive learning-based image deraining method that
investigates the correlation between rainy and clear images and leverages a
contrastive prior to optimize the mutual information of the rainy and restored
counterparts. Given the complex and varied real-world rain patterns, we develop
a recursive mechanism. It involves multi-scale feature extraction and dynamic
cross-level information recruitment modules. The former advances the portrayal
of diverse rain patterns more precisely, while the latter can selectively
compensate high-level features for shallow-level information. We term the
proposed recursive dynamic multi-scale network with a contrastive prior, RDMC.
Extensive experiments on synthetic benchmarks and real-world images demonstrate
that the proposed RDMC delivers strong performance on the depiction of rain
streaks and outperforms the state-of-the-art methods. Moreover, a practical
evaluation of object detection and semantic segmentation shows the
effectiveness of the proposed method.Comment: 13 pages, 16 figure
Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction
Photographs taken with less-than-ideal exposure settings often display poor
visual quality. Since the correction procedures vary significantly, it is
difficult for a single neural network to handle all exposure problems.
Moreover, the inherent limitations of convolutions, hinder the models ability
to restore faithful color or details on extremely over-/under- exposed regions.
To overcome these limitations, we propose a Macro-Micro-Hierarchical
transformer, which consists of a macro attention to capture long-range
dependencies, a micro attention to extract local features, and a hierarchical
structure for coarse-to-fine correction. In specific, the complementary
macro-micro attention designs enhance locality while allowing global
interactions. The hierarchical structure enables the network to correct
exposure errors of different scales layer by layer. Furthermore, we propose a
contrast constraint and couple it seamlessly in the loss function, where the
corrected image is pulled towards the positive sample and pushed away from the
dynamically generated negative samples. Thus the remaining color distortion and
loss of detail can be removed. We also extend our method as an image enhancer
for low-light face recognition and low-light semantic segmentation. Experiments
demonstrate that our approach obtains more attractive results than
state-of-the-art methods quantitatively and qualitatively.Comment: Accepted by ACM MM 202
From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion
With the rapid progression of deep learning technologies, multi-modality
image fusion has become increasingly prevalent in object detection tasks.
Despite its popularity, the inherent disparities in how different sources
depict scene content make fusion a challenging problem. Current fusion
methodologies identify shared characteristics between the two modalities and
integrate them within this shared domain using either iterative optimization or
deep learning architectures, which often neglect the intricate semantic
relationships between modalities, resulting in a superficial understanding of
inter-modal connections and, consequently, suboptimal fusion outcomes. To
address this, we introduce a text-guided multi-modality image fusion method
that leverages the high-level semantics from textual descriptions to integrate
semantics from infrared and visible images. This method capitalizes on the
complementary characteristics of diverse modalities, bolstering both the
accuracy and robustness of object detection. The codebook is utilized to
enhance a streamlined and concise depiction of the fused intra- and
inter-domain dynamics, fine-tuned for optimal performance in detection tasks.
We present a bilevel optimization strategy that establishes a nexus between the
joint problem of fusion and detection, optimizing both processes concurrently.
Furthermore, we introduce the first dataset of paired infrared and visible
images accompanied by text prompts, paving the way for future research.
Extensive experiments on several datasets demonstrate that our method not only
produces visually superior fusion results but also achieves a higher detection
mAP over existing methods, achieving state-of-the-art results.Comment: 10 pages, 12 figures, 3 tables, conferenc
Complexity measures and uncertainty relations of the high-dimensional harmonic and hydrogenic systems
In this work we find that not only the Heisenberg-like uncertainty products
and the R\'enyi-entropy-based uncertainty sum have the same first-order values
for all the quantum states of the -dimensional hydrogenic and
oscillator-like systems, respectively, in the pseudoclassical ()
limit but a similar phenomenon also happens for both the
Fisher-information-based uncertainty product and the Shannon-entropy-based
uncertainty sum, as well as for the Cr\'amer-Rao and Fisher-Shannon
complexities. Moreover, we show that the LMC (L\'opez-Ruiz-Mancini-Calvet) and
LMC-R\'enyi complexity measures capture the hydrogenic-harmonic difference in
the high dimensional limit already at first order
Activation of Orexin System Stimulates CaMKII Expression
Hyperactivity of the orexin system within the paraventricular nucleus (PVN) has been shown to contribute to increased sympathetic nerve activity (SNA) and blood pressure (BP) in rodent animals. However, the underlying molecular mechanisms remain unclear. Here, we test the hypothesis that orexin system activation stimulates calcium/calmodulin-dependent kinase II (CaMKII) expression and activation, and stimulation of CaMKII expressing PVN neurons increases SNA and BP. Real-time PCR and/or western blot were carried out to test the effect of orexin-A administration on CaMKII expression in the PVN of normal Sprague Dawley (SD) rats and orexin receptor 1 (OX1R) expressing PC12 cells. Immunostaining was performed to assess OX1R cellular localization in the PVN of SD rats as well as orexin-A treatment on CaMKII activation in cultured hypothalamic neurons. In vivo sympathetic nerve recordings were employed to test the impact of optogenetic stimulation of CaMKII-expressing PVN neurons on the renal SNA (RSNA) and BP. The results showed that intracerebroventricular injection of orexin-A into the SD rat increases mRNA expression of CaMKII subunits in the PVN. In addition, Orexin-A treatment increases CaMKII expression and its phosphorylation in OX1R-expressing PC12 cells. Furthermore, Orexin-A treatment increases CaMKII activation in cultured hypothalamic neurons from neonatal SD rats. Finally, optogenetic excitation of PVN CaMKII-expressing neurons results in robust increases in RSNA and BP in SD rats. Our results suggest that increased orexin system activity activates CaMKII expression in cardiovascular relevant regions, and this may be relevant to the downstream cardiovascular effects of CaMKII
Complex Spatial Dynamics of Oncolytic Viruses In Vitro: Mathematical and Experimental Approaches
Oncolytic viruses replicate selectively in tumor cells and can serve as targeted treatment agents. While promising results have been observed in clinical trials, consistent success of therapy remains elusive. The dynamics of virus spread through tumor cell populations has been studied both experimentally and computationally. However, a basic understanding of the principles underlying virus spread in spatially structured target cell populations has yet to be obtained. This paper studies such dynamics, using a newly constructed recombinant adenovirus type-5 (Ad5) that expresses enhanced jellyfish green fluorescent protein (EGFP), AdEGFPuci, and grows on human 293 embryonic kidney epithelial cells, allowing us to track cell numbers and spatial patterns over time. The cells are arranged in a two-dimensional setting and allow virus spread to occur only to target cells within the local neighborhood. Despite the simplicity of the setup, complex dynamics are observed. Experiments gave rise to three spatial patterns that we call “hollow ring structure”, “filled ring structure”, and “disperse pattern”. An agent-based, stochastic computational model is used to simulate and interpret the experiments. The model can reproduce the experimentally observed patterns, and identifies key parameters that determine which pattern of virus growth arises. The model is further used to study the long-term outcome of the dynamics for the different growth patterns, and to investigate conditions under which the virus population eliminates the target cells. We find that both the filled ring structure and disperse pattern of initial expansion are indicative of treatment failure, where target cells persist in the long run. The hollow ring structure is associated with either target cell extinction or low-level persistence, both of which can be viewed as treatment success. Interestingly, it is found that equilibrium properties of ordinary differential equations describing the dynamics in local neighborhoods in the agent-based model can predict the outcome of the spatial virus-cell dynamics, which has important practical implications. This analysis provides a first step towards understanding spatial oncolytic virus dynamics, upon which more detailed investigations and further complexity can be built
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