scholarly journals Iterative guided image fusion

2016 ◽  
Vol 2 ◽  
pp. e80 ◽  
Author(s):  
Alexander Toet
Keyword(s):  
Image Fusion ◽  
Spatial Scale ◽  
Spatial Scales ◽  
Small Scale ◽  
Computationally Efficient ◽  
Multi Scale ◽  
Saliency Maps ◽  
Spatial Consistency ◽  
Guided Filtering ◽  
Fusion Scheme

We propose a multi-scale image fusion scheme based on guided filtering. Guided filtering can effectively reduce noise while preserving detail boundaries. When applied in an iterative mode, guided filtering selectively eliminates small scale details while restoring larger scale edges. The proposed multi-scale image fusion scheme achieves spatial consistency by using guided filtering both at the decomposition and at the recombination stage of the multi-scale fusion process. First, size-selective iterative guided filtering is applied to decompose the source images into approximation and residual layers at multiple spatial scales. Then, frequency-tuned filtering is used to compute saliency maps at successive spatial scales. Next, at each spatial scale binary weighting maps are obtained as the pixelwise maximum of corresponding source saliency maps. Guided filtering of the binary weighting maps with their corresponding source images as guidance images serves to reduce noise and to restore spatial consistency. The final fused image is obtained as the weighted recombination of the individual residual layers and the mean of the approximation layers at the coarsest spatial scale. Application to multiband visual (intensified) and thermal infrared imagery demonstrates that the proposed method obtains state-of-the-art performance for the fusion of multispectral nightvision images. The method has a simple implementation and is computationally efficient.

Small spatial scale effects of wave action exposure on morphological traits of the limpet Lottia subrugosa

2019 ◽  
Vol 99 (06) ◽  
pp. 1309-1315
Author(s):  
Edson A. Vieira ◽  
Marília Bueno
Keyword(s):  
Spatial Scale ◽  
Scale Effects ◽  
Spatial Scales ◽  
Wave Exposure ◽  
Wave Action ◽  
Shell Shape ◽  
Small Scale ◽  
Small Spatial Scale ◽  
Large Spatial Scale ◽  
Exposed Shore

AbstractMany studies have already assessed how wave action may affect morphology of intertidal species among sites that vary in wave exposure, but few attempted to look to this issue in smaller scales. Using the most common limpet of the Brazilian coast, Lottia subrugosa, and assuming position on rocky boulders as a proxy for wave action at small scale, we tested the hypothesis that waves may also influence limpet morphology at a smaller spatial scale by investigating how individual size, foot area and shell shape vary between sheltered and exposed boulder sides on three shores in the coast of Ubatuba, Brazil. Limpets consistently showed a proportionally larger foot on exposed boulder sides for all shores, indicating that stronger attachment is an important mechanism to deal with wave action dislodgement at a smaller scale. Shell shape also varied in the scale investigated here, with more conical (dissipative) shells occurring in exposed boulder sides in one exposed shore across time and in the other exposed shore in one year. Shell shape did not vary regarding boulder sides across time in the most sheltered shore. Although we did not assess large spatial scale effects of wave action in this study, variations of the effect of waves at small spatial scale observed for shell shape suggest that it may be modulated by the local wave exposure regime. Our work highlights the importance of wave action at small spatial scales, and may help to understand the ecological variability of limpets inhabiting rocky shores.

scholarly journals Quantification of Myocyte Disarray in Human Cardiac Tissue

2021 ◽  
Vol 12 ◽  
Author(s):  
Francesco Giardini ◽  
Erica Lazzeri ◽  
Giulia Vitale ◽  
Cecilia Ferrantini ◽  
Irene Costantini ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Cardiac Tissue ◽  
Spatial Scales ◽  
Three Dimensional ◽  
Structural Data ◽  
Multi Scale ◽  
Tissue Organization ◽  
Human Cardiac Tissue ◽  
Microscopy Techniques ◽  
Scale Of Analysis

Proper three-dimensional (3D)-cardiomyocyte orientation is important for an effective tension production in cardiac muscle. Cardiac diseases can cause severe remodeling processes in the heart, such as cellular misalignment, that can affect both the electrical and mechanical functions of the organ. To date, a proven methodology to map and quantify myocytes disarray in massive samples is missing. In this study, we present an experimental pipeline to reconstruct and analyze the 3D cardiomyocyte architecture in massive samples. We employed tissue clearing, staining, and advanced microscopy techniques to detect sarcomeres in relatively large human myocardial strips with micrometric resolution. Z-bands periodicity was exploited in a frequency analysis approach to extract the 3D myofilament orientation, providing an orientation map used to characterize the tissue organization at different spatial scales. As a proof-of-principle, we applied the proposed method to healthy and pathologically remodeled human cardiac tissue strips. Preliminary results suggest the reliability of the method: strips from a healthy donor are characterized by a well-organized tissue, where the local disarray is log-normally distributed and slightly depends on the spatial scale of analysis; on the contrary, pathological strips show pronounced tissue disorganization, characterized by local disarray significantly dependent on the spatial scale of analysis. A virtual sample generator is developed to link this multi-scale disarray analysis with the underlying cellular architecture. This approach allowed us to quantitatively assess tissue organization in terms of 3D myocyte angular dispersion and may pave the way for developing novel predictive models based on structural data at cellular resolution.

scholarly journals seNorge_2018, daily precipitation and temperature datasets over Norway

2019 ◽  
Author(s):  
Cristian Lussana ◽  
Ole Einar Tveito ◽  
Andreas Dobler ◽  
Ketil Tunheim
Keyword(s):  
Spatial Scale ◽  
Climate Model ◽  
Spatial Scales ◽  
Small Scale ◽  
Total Precipitation ◽  
Climate Data ◽  
Wide Range ◽  
Analysis Quality ◽  
Station Density ◽  
Daily Total

Abstract. seNorge_2018 is a collection of observational gridded datasets over Norway for: daily total precipitation; daily mean, maximum and minimum temperatures. The time period covers 1957 to 2017, and the data are presented over a high-resolution terrain-following grid with 1 km spacing in both meridional and zonal directions. The seNorge family of observational gridded datasets developed at the Norwegian Meteorological Institute (MET Norway) has a twenty-year long history and seNorge_2018 is its newest member, the first providing daily minimum and maximum temperatures. seNorge datasets are used for a wide range of applications in climatology, hydrology and meteorology. The observational dataset is based on MET Norway's climate data, which has been integrated by the European Climate Assessment and Dataset database. Two distinct statistical interpolation methods have been developed, one for temperature and the other for precipitation. They are both based on a spatial scale-separation approach where, at first, the analysis (i.e., predictions) at larger spatial scales are estimated. Subsequently they are used to infer the small-scale details down to a spatial scale comparable to the local observation density. Mean, maximum and minimum temperatures are interpolated separately, then physical consistency among them is enforced. For precipitation, in addition to observational data, the spatial interpolation makes use of information provided by a climate model. The analysis evaluation is based on cross-validation statistics and comparison with a previous seNorge version. The analysis quality is presented as a function of the local station density. We show that the occurrence of large errors in the analyses decays at an exponential rate with the increase in the station density. Temperature analyses over most of the domain are generally not affected by significant biases. However, during wintertime in data-sparse regions the analyzed minimum temperatures do have a bias between 2 °C and 3 °C. Minimum temperatures are more challenging to represent and large errors are more frequent than for maximum and mean temperatures. The precipitation analysis quality depends crucially on station density: the frequency of occurrence of large errors for intense precipitation is less than 5 % in data-dense regions, while it is approximately 30 % in data-sparse regions. he open-access datasets are available20for public download at: daily total precipitation (DOI: https://doi.org/10.5281/zenodo.2082320, Lussana, 2018b); daily mean (DOI: https://doi.org/10.5281/zenodo.2023997, Lussana, 2018c) , maximum (DOI: https://doi.org/10.5281/zenodo.2559372, Lussana, 2018e) and minimum (DOI: https://doi.org/10.5281/zenodo.2559354, Lussana, 2018d) temperatures.

scholarly journals Relationship between small-scale catch-per-unit-effort and abundance in New Zealand abalone (pāua, Haliotis iris) fisheries

2015 ◽  
Author(s):  
Edward R Abraham ◽  
Philipp Neubauer
Keyword(s):  
Spatial Scale ◽  
Spatial Information ◽  
Spatial Scales ◽  
Small Scale ◽  
Gps Data ◽  
Catch Per Unit Effort ◽  
Small Spatial Scale ◽  
Unit Effort ◽  
Data Loggers ◽  
Gps Data Loggers

Catch-per-unit-effort (CPUE) is commonly used as an index of abundance in fishery stock assessments, but CPUE may be misleading, as a number of global fishery collapses have been attributed to a hyper-stable CPUE. In abalone (Halitidae family) fisheries, CPUE at large spatial scales may be hyper-stable because of aggregating behaviour and serial-depletion, whereby fishers sequentially fish areas with no corresponding decline in CPUE. Obtaining detailed spatial information in abalone fisheries might mitigate this problem, allowing CPUE to be used more confidently in these fisheries. Here, we report on the use of newly-developed high-resolution Global Positioning System (GPS) data loggers in New Zealand's blacklip abalone (pāua, Haliotis iris) fisheries. Using these data loggers, we tested, via a fish-down experiment, if CPUE is a reliable indicator of abundance at a small spatial scale and over a period of months. In the experiment, hyper-stability at small spatial scales occurred at high abundance, but CPUE reflected the estimated depletion level at the end of experimental fishing. This experiment suggests that the GPS data loggers provide a promising avenue to track CPUE at a small spatial scale, and to assess spatial resource use in New Zealand's pāua fisheries.

scholarly journals Spatial Scale Dependence of Ecological Factors That Regulate Functional and Phylogenetic Assembly in a Mediterranean High Mountain Grassland

2021 ◽  
Vol 9 ◽  
Author(s):  
David S. Pescador ◽  
Francesco de Bello ◽  
Jesús López-Angulo ◽  
Fernando Valladares ◽  
Adrián Escudero
Keyword(s):  
Global Warming ◽  
Organic Carbon ◽  
Spatial Scale ◽  
Community Assembly ◽  
Species Interactions ◽  
Species Turnover ◽  
Spatial Scales ◽  
Ecological Factors ◽  
Functional Heterogeneity ◽  
Multi Scale

Understanding how functional and phylogenetic patterns vary among scales and along ecological gradients within a given species pool is critical for inferring community assembly processes. However, we lack a clear understanding of these patterns in stressful habitats such as Mediterranean high mountains where ongoing global warming is expected to affect species fitness and species interactions, and subsequently species turnover. In this study, we investigated 39 grasslands with the same type of plant community and very little species turnover across an elevation gradient above the treeline at Sierra de Guadarrama National Park in central Spain. In particular, we assessed functional and phylogenetic patterns, including functional heterogeneity, using a multi-scale approach (cells, subplots, and plots) and determined the relevance of key ecological factors (i.e., elevation, potential solar radiation, pH, soil organic carbon, species richness, and functional heterogeneity) that affect functional and phylogenetic patterns at each spatial scale. Overall, at the plot scale, coexisting species tended to be more functionally and phylogenetically similar. By contrast, at the subplot and cell scales, species tended to be more functionally different but phylogenetically similar. Functional heterogeneity at the cell scale was comparable to the variation across plots along the gradient. The relevance of ecological factors that regulate diversity patterns varied among spatial scales. An increase in elevation resulted in functional clustering at larger scales and phylogenetic overdispersion at a smaller scale. The soil pH and organic carbon levels exhibited complex functional patterns, especially at small spatial scales, where an increase in pH led to clustering patterns for the traits related to the leaf economic spectrum (i.e., foliar thickness, specific leaf area, and leaf dry matter content). Our findings confirm the presence of primary environmental filters (coldness and summer drought at our study sites) that constrain the regional species pool, suggesting the presence of additional assembly mechanisms that act at the smallest scale (e.g., micro-environmental gradients and/or species interactions). Functional and phylogenetic relatedness should be determined using a multi-scale approach to help interpret community assembly processes and understand the initial community responses to environmental changes, including ongoing global warming.

scholarly journals Observed small spatial scale and seasonal variability of the CO<sub>2</sub> system in the Southern Ocean

2014 ◽  
Vol 11 (1) ◽  
pp. 75-90 ◽  
Author(s):  
L. Resplandy ◽  
J. Boutin ◽  
L. Merlivat
Keyword(s):  
Southern Ocean ◽  
Spatial Scale ◽  
Large Scale ◽  
Carbon Budget ◽  
Spatial Scales ◽  
Mixed Layer Depth ◽  
Small Scale ◽  
Spatiotemporal Resolution ◽  
Small Spatial Scale ◽  
Data Set

Abstract. The considerable uncertainties in the carbon budget of the Southern Ocean are largely attributed to unresolved variability, in particular at a seasonal timescale and small spatial scale (~ 100 km). In this study, the variability of surface pCO2 and dissolved inorganic carbon (DIC) at seasonal and small spatial scales is examined using a data set of surface drifters including ~ 80 000 measurements at high spatiotemporal resolution. On spatial scales of 100 km, we find gradients ranging from 5 to 50 μatm for pCO2 and 2 to 30 μmol kg−1 for DIC, with highest values in energetic and frontal regions. This result is supported by a second estimate obtained with sea surface temperature (SST) satellite images and local DIC–SST relationships derived from drifter observations. We find that dynamical processes drive the variability of DIC at small spatial scale in most regions of the Southern Ocean and the cascade of large-scale gradients down to small spatial scales, leading to gradients up to 15 μmol kg−1 over 100 km. Although the role of biological activity is more localized, it enhances the variability up to 30 μmol kg−1 over 100 km. The seasonal cycle of surface DIC is reconstructed following Mahadevan et al. (2011), using an annual climatology of DIC and a monthly climatology of mixed layer depth. This method is evaluated using drifter observations and proves to be a reasonable first-order estimate of the seasonality in the Southern Ocean that could be used to validate model simulations. We find that small spatial-scale structures are a non-negligible source of variability for DIC, with amplitudes of about a third of the variations associated with the seasonality and up to 10 times the magnitude of large-scale gradients. The amplitude of small-scale variability reported here should be kept in mind when inferring temporal changes (seasonality, interannual variability, decadal trends) of the carbon budget from low-resolution observations and models.

scholarly journals The importance of different spatial scales in determining structure and function of deep-sea infauna communities

2013 ◽  
Vol 10 (1) ◽  
pp. 195-232 ◽  
Author(s):  
J. Ingels ◽  
A. Vanreusel
Keyword(s):  
Spatial Scale ◽  
Deep Sea ◽  
Spatial Ecology ◽  
Spatial Scales ◽  
Benthic Communities ◽  
Standing Stock ◽  
Small Scale ◽  
Sampling Locations ◽  
And Function ◽  
Structure Diversity

Abstract. The urge to understand spatial distributions of species and communities and their causative processes has continuously instigated the development and testing of conceptual models in spatial ecology. For the deep-sea, there is evidence that structure, diversity and function of benthic communities are regulated by a multitude of biotic and environmental processes that act in concert on different spatial scales, but the spatial patterns are poorly understood compared to those for other ecosystems. Deep-sea studies generally focus on very limited scale-ranges, thereby impairing our understanding of which spatial scales and associated processes are most important in driving diversity and ecosystem function of communities. Here, we used an extensive integrated dataset of free-living nematodes from deep-sea sediments to unravel which spatial scale is most important in determining benthic infauna communities. Multiple-factor multivariate permutational analyses were performed on different sets of community descriptors (structure, diversity, function, standing stock). The different spatial scales investigated cover two margins in the Northeast Atlantic, several submarine canyons/channel/slope areas, a bathymetrical range of 700–4300 m (represents different stations, 5–50 km apart), different sampling locations at each station (replication distances, 1–200 m), and vertical sediment profiles (cm layers). The results indicated that the most important spatial scale for diversity, functional and standing stock variability is the smallest one; infauna communities changed substantially more with differences between sediment depth layers than with differences associated to larger geographical or bathymetrical scales. Community structure differences were largest between stations at both margins. Important regulating ecosystem processes and the scale on which they occur are discussed. The results imply that, if we are to improve our understanding of ecosystem patterns of deep-sea infauna and the relevant processes driving their structure, diversity, function and standing stock, we must pay particular attention to the small-scale heterogeneity or patchiness and the causative mechanisms acting on that scale.

Cloud-Based Image Fusion Using Guided Filtering

2016 ◽  
pp. 146-165
Author(s):  
Piyush Kumar Shukla ◽  
Madhuvan Dixit
Keyword(s):  
Image Fusion ◽  
Image Coding ◽  
Large Scale ◽  
Weighted Average ◽  
Spatial Consistency ◽  
Novel Approach ◽  
Guided Filtering ◽  
Fused Image ◽  
Image Fusion Technique ◽  
Highly Correlated

Current image coding with image fusion schemes make it hard to utilize external images for transform even if highly correlated images can be found in the cloud. To solve this problem, we explain an approach of cloud-based image transform coding with image fusion methodwhich is distinguish from exists image fusion method. A fast and efficient image fusion technique is proposed for creating a highly generated fused image through merging multiple corresponding images. The proposed technique is based on a two-scale decomposition of an image into a low layer containing large scale variations, and a detail layer acquiring small scale details. A novel approach of guided filtering-based weighted average method is proposed to make full use of spatial consistency for merge of the base and detail layers. Analytical results represent that the proposed technique can obtain state-of-the-art performance for image fusion of multispectral, multifocus, multimodal, and multiexposure images.

scholarly journals Spatial Scale of Geomagnetic Pc5/pi3 Pulsations as a Factor of Their Efficiency in Generation of Geomagnetically Induced Currents

2020 ◽  
Author(s):  
Nadezda V. Yagova ◽  
Vyacheslav Pilipenko ◽  
Yaroslav Sakharov ◽  
Vasily Selivanov
Keyword(s):  
Electric Power ◽  
Spatial Scale ◽  
Large Scale ◽  
Power Transmission ◽  
Spatial Scales ◽  
Power Line ◽  
Small Scale ◽  
Geomagnetically Induced Currents ◽  
Electric Power Line ◽  
Induced Currents

Abstract Geomagnetically induced currents (GICs) in a meridional power transmission line at the Kola Peninsula are analyzed during the intervals of Pc5/Pi3 (frequency range from 1.5 to 5 mHz) pulsation activity observed at the IMAGE magnetometer network. We have analyzed GIC in a transformer at the terminal station Vykhodnoj (68◦N, 33◦E) during the entire year of 2015, near the maximum of 24-th Solar cycle. To quantify the efficiency of GIC generation by a geomagnetic pulsation, a ratio between power spectral densities of GIC and magnetic field variations is introduced. Upon examination of the efficiency of geomagnetic pulsations in GIC generation, the emphasis is given to its dependence on frequency and spatial scale. To estimate pulsation spatial scales in latitudinal and longitudinal directions, the triangle of stations KEV-SOD-KIL has been used. Large-scale pulsations along the electric power line (with a high spectral coherence, low phase difference, and similar amplitudes) are found to be more effective in GIC generation than small-scale pulsations. The accuracy of GIC prediction also depends on the pulsation scale transversal to the electric power line.

Sparse Features with Fast Guided Filtering for Medical Image Fusion

2020 ◽  
Vol 10 (5) ◽  
pp. 1195-1204
Author(s):  
Yongxin Zhang ◽  
Deguang Li ◽  
Ruiling Zhang ◽  
Yan Cui
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Sports Injuries ◽  
Medical Images ◽  
Low Rank ◽  
Convex Programs ◽  
Medical Image Fusion ◽  
Saliency Maps ◽  
Guided Filtering ◽  
Sparse Features

In medical image fusion, partially focused images are combined into a completely informative in focus image of the same scene, helping the clinical applicability of medical image for assessment on sports injuries diagnosis and rehabilitation. However, many state-of-the-art fusion methods seldom consider structural differences between guidance and input images, so not all the significant features of the source images can be well preserved for a completely informative focused image. In this paper, a fusion method via fast guided filtering with sparse features is proposed. The source images are decomposed into base and detail layers using an average filter. They are also split into low-rank and sparse parts by solving convex programs. Saliency maps are constructed based on the sparse features using low pass and high pass filters. Fast guided filtering is used to optimize saliency maps for constructing weighted maps of the base and detail layers, and for maintaining the spatial consistency between the source images and the layers. The fused base and detail layers are integrated to construct the final fused image. Experiments on nine pair of medical images demonstrate that the proposed method achieved better medical images compared to other reported methods in terms of both subjective and objective evaluations.

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