Denoising RENOIR Image Dataset with DBSR

Noise reduction algorithms have often been evaluated using images degraded by artificially synthesised noise. The RENOIR image dataset [3] provides an alternative way for testing noise reduction algorithms on real noisy images and we propose in this paper to assess our CNN called De-Blurring Super-Resolution (DBSR) [2] to reduce the natural noise due to low light conditions in a RENOIR dataset

Harmonic Convolutional Networks based on Discrete Cosine Transform

Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. We propose to learn these filters as combinations of preset spectral filters defined by the Discrete Cosine Transform (DCT). Our proposed DCT-based harmonic blocks replace conventional convolutional layers to produce partially or fully harmonic versions of new or existing CNN architectures. Using DCT energy compaction properties, we demonstrate how the harmonic networks can be efficiently compressed by truncating high-frequency information in harmonic blocks thanks to the redundancies in the spectral domain. We report extensive experimental validation demonstrating benefits of the introduction of harmonic blocks into state-of-the-art CNN models in image classification, object detection and semantic segmentation applications.Comment: arXiv admin note: substantial text overlap with arXiv:1812.0320

Harmonic Networks for Image Classification

Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. In contrast, in this paper we propose harmonic blocks that produce features by learning optimal combinations of responses to preset spectral filters. We rely on the use of the Discrete Cosine Transform filters which have excellent energy compaction properties and are widely used for image compression. The proposed harmonic blocks are intended to replace conventional convolutional layers to produce partially or fully harmonic versions of new or existing CNN architectures. We demonstrate how the harmonic networks can be efficiently compressed by exploiting redundancy in spectral domain and truncating high-frequency information. We extensively validate our approach and show that the introduction of harmonic blocks into state-of-the-art CNN models results in improved classification performance on CIFAR and ImageNet datasets

On the New "Manual on Design of Composite Steel and Concrete Structures (in Elaboration of Formulary SP 266.13330.2016 "Composite Steel and Concrete Structures. Design Rules")"

The paper provides a brief overview of domestic and foreign guidelines (manuals) for the design of composite steel and concrete structures: steel-concrete slabs on profiled flooring, combined beams, and columns with rigid reinforcement. The necessity of creation of the actual manual corresponding to the modernlevel of development of construction science, normative documents and design practiceslinked to the new formulary SP 266.1325800.2016 is proved. It will facilitate the design, reduce labor expenditures and improve the reliability of composite steel and concrete structures. The new guidance provides general recommendations for the design of composite steel and concrete structures and the basic regulations for the calculations. The new guidance describes recommendations for modeling of composite steel and concrete structures and elements in the calculated complexes, the recommendations for calculation of combined beams fully concreting rectangular and T-section, partially concreting along with support slab on the lower flange of the beam, columns with rigid reinforcement, shear a connection of composite beams. Recommendations on the registration of creep, shrinkage and crack formation in the appointment of the modulus of elasticity are given. Recommendations on the use of diagrams of the state of concrete, reinforcement, and steel in the calculation of steel-concrete elements on a nonlinear deformation model are given. Recommendations on the use of the range of sheet flooring for steel-reinforced concrete slabs, as well as metal profiles as steel beams and rigid reinforcement in the cross sections of columns and combined beams, are presented. Recommendations on a design of units and details of composite steel and concrete structures are given, refined recommendations on buffer are presented. The examples of connection of steel beams with columns with rigid reinforcement are given. The examples of calculation of composite steel and concrete structures taking subject to the recommendations given in the Manual are presented

IM2ELEVATION: Building Height Estimation from Single-View Aerial Imagery

Estimation of the Digital Surface Model (DSM) and building heights from single-view aerial imagery is a challenging inherently ill-posed problem that we address in this paper by resorting to machine learning. We propose an end-to-end trainable convolutional-deconvolutional deep neural network architecture that enables learning mapping from a single aerial imagery to a DSM for analysis of urban scenes. We perform multisensor fusion of aerial optical and aerial light detection and ranging (Lidar) data to prepare the training data for our pipeline. The dataset quality is key to successful estimation performance. Typically, a substantial amount of misregistration artifacts are present due to georeferencing/projection errors, sensor calibration inaccuracies, and scene changes between acquisitions. To overcome these issues, we propose a registration procedure to improve Lidar and optical data alignment that relies on Mutual Information, followed by Hough transform-based validation step to adjust misregistered image patches. We validate our building height estimation model on a high-resolution dataset captured over central Dublin, Ireland: Lidar point cloud of 2015 and optical aerial images from 2017. These data allow us to validate the proposed registration procedure and perform 3D model reconstruction from single-view aerial imagery. We also report state-of-the-art performance of our proposed architecture on several popular DSM estimation datasets

Unsupervised Learning of Generalized Gamma Mixture Model with Application in Statistical Modeling of High-Resolution SAR Images

International audienceThe accurate statistical modeling of synthetic aperture radar (SAR) images is a crucial problem in the context of effective SAR image processing, interpretation and application. In this paper a semi-parametric approach is designed within the framework of finite mixture models based on the generalized Gamma distribution (GΓD) in view of its flexibility and compact form. Specifically, we develop a generalized Gamma mixture model (GΓMM) to implement an effective statistical analysis of high-resolution SAR images and prove the identifiability of such mixtures. A low-complexity unsupervised estimation method is derived by combining the proposed histogram-based expectation-conditional maximization (ECM) algorithm and the Figueiredo-Jain algorithm. This results in a numerical maximum likelihood (ML) estimator that can simultaneously determine the ML estimates of component parameters and the optimal number of mixture components. Finally, the state-of-the-art performance of this proposed method is verified by experiments with a wide range of high-resolution SAR images. Index Terms Synthetic aperture radar (SAR) images, finite mixture model, generalized Gamma distribution, expectation-conditional maximization (ECM) algorithm, minimum message length (MML), probability density function estimation , unsupervised learning

Estimating Hemodynamic Significant Deformations of Brachiocephalic Arteries Using CT Perfusion

Background and Aim: The cause of most strokes is associated with the pathology of the carotid arteries. Many modern researchers suggest preventive surgery in the presence of arterial deformity to prevent strokes. The hemodynamic significance of carotid deformities is determined by morphological and functional disorders at the level of tortuosity, the reaction of cerebral blood flow is not often considered. Here, we assume that cerebral autoregulation in tortuosity can be different. Methods and Materials/Patients: A total of 64 patients (31-75 years old) with 110 carotid deformities were analyzed. Duplex color mapping, computed tomography angiography of carotid arteries, and computed tomography perfusion were performed by estimating the absolute and average values of cerebral blood flow (mL/100 g/min), cerebral blood volume (mL/100 g), Mean transit time (MTT) (s) in similar areas of the cortex. In 6 patients, the acetazolamide challenge test was used to evaluate the autoregulatory disturbances. Results: According to computed tomography angiography and duplex color mapping, 18(28.1%) patients had unilateral tortuosity, and 46(71.9%) patients had bilateral tortuo s ity. Hemodynamically significant deformities were detected in 33 cases (30% of tortuosity). In 54 cases (49% of tortuosity), the deformities were accompanied by carotid stenosis. Perfusion disorders were detected in 23 of 64 patients (35.9%). In the majority of cases (75% of all perfusion disorders), hypoperfusion was diagnosed on the side corresponding to the maximum degree of stenosis, regardless of the location of the tortuosity. Neurologically significant hypoperfusion, compensated by collateral blood flow revealed only in 7.8% of cases of hemodynamic significant internal carotid artery deformity without concomitant atherosclerosis. Conclusion: The decision on surgical correction of carotid artery tortuosity should be made while considering both local changes in hemodynamics and proven violations of autoregulation of cerebral blood flow, especially in patients with concomitant carotid stenosis

Ocean-bottom seismographs based on broadband MET sensors: architecture and deployment case study in the Arctic

The Arctic seas are now of particular interest due to their prospects in terms of hydrocarbon extraction, development of marine transport routes, etc. Thus, various geohazards, including those related to seismicity, require detailed studies, especially by instrumental methods. This paper is devoted to the ocean-bottom seismographs (OBS) based on broadband molecular–electronic transfer (MET) sensors and a deployment case study in the Laptev Sea. The purpose of the study is to introduce the architecture of several modifications of OBS and to demonstrate their applicability in solving different tasks in the framework of seismic hazard assessment for the Arctic seas. To do this, we used the first results of several pilot deployments of the OBS developed by Shirshov Institute of Oceanology of the Russian Academy of Sciences (IO RAS) and IP Ilyinskiy A.D. in the Laptev Sea that took place in 2018–2020. We highlighted various seismological applications of OBS based on broadband MET sensors CME-4311 (60 s) and CME-4111 (120 s), including the analysis of ambient seismic noise, registering the signals of large remote earthquakes and weak local microearthquakes, and the instrumental approach of the site response assessment. The main characteristics of the broadband MET sensors and OBS architectures turned out to be suitable for obtaining high-quality OBS records under the Arctic conditions to solve seismological problems. In addition, the obtained case study results showed the prospects in a broader context, such as the possible influence of the seismotectonic factor on the bottom-up thawing of subsea permafrost and massive methane release, probably from decaying hydrates and deep geological sources. The described OBS will be actively used in further Arctic expeditions