Comparison of Healing Effect on Biomedical Signals by the Difference of Color Gamut and Dynamic Range When Viewing Nature Images

2021 ◽  
Author(s):  
Miho Shinohara ◽  
Eriko Ishii ◽  
Mitsuho Yamada ◽  
Yuko Hoshino
Keyword(s):  
Dynamic Range ◽  
Color Gamut ◽  
Biomedical Signals ◽  
Healing Effect ◽  
The Difference

Analysis of the Healing Effect of Walking Activities According to the Difference in FOREST ENVIRONMENT

J-Institute ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 1-16
Author(s):  
Dong-suk Sung ◽  
Ju-sik Park ◽  
Won-hyeon Lim
Keyword(s):  
Forest Environment ◽  
Healing Effect ◽  
The Difference

scholarly journals Improvements to the Geostationary Visible Imager Ray-Matching Calibration Algorithm for CERES Edition 4

2016 ◽  
Vol 33 (12) ◽  
pp. 2679-2698 ◽  
Author(s):  
David R. Doelling ◽  
Conor O. Haney ◽  
Benjamin R. Scarino ◽  
Arun Gopalan ◽  
Rajendra Bhatt
Keyword(s):  
Dynamic Range ◽  
Spectral Band ◽  
Radiant Energy ◽  
Convective Cloud ◽  
Energy System ◽  
Processing Scheme ◽  
Matching Algorithm ◽  
Cloud Properties ◽  
Calibration Algorithm ◽  
The Difference

AbstractThe Clouds and the Earth’s Radiant Energy System (CERES) project relies on geostationary imager–derived TOA broadband fluxes and cloud properties to account for the regional diurnal fluctuations between the Terra and Aqua CERES and MODIS measurements. The CERES project employs a ray-matching calibration algorithm in order to transfer the Aqua MODIS calibration to the geostationary (GEO) imagers, thereby allowing the derivation of consistent fluxes and cloud retrievals across the 16 GEO imagers utilized in the CERES record. The CERES Edition 4 processing scheme grants the opportunity to recalibrate the GEO record using an improved GEO/MODIS all-sky ocean ray-matching algorithm. Using a graduated angle matching method, which is most restrictive for anisotropic clear-sky ocean radiances and least restrictive for isotropic bright cloud radiances, reduces the bidirectional bias while preserving the dynamic range. Furthermore, SCIAMACHY hyperspectral radiances are used to account for both the solar incoming and Earth-reflected spectra in order to correct spectral band differences. As a result, the difference between the linear regression offset and the maintained GEO space count was reduced, and the calibration slopes computed from the linear fit and the regression through the space count agreed to within 0.4%. A deep convective cloud (DCC) ray-matching algorithm is also presented. The all-sky ocean and DCC ray-matching timeline gains are within 0.7% of one another. Because DCC are isotropic and the brightest, Earth targets with near-uniform visible spectra, the temporal standard error of GEO imager gains, are reduced by up to 60% from that of all-sky ocean targets.

A Study of Color Variance between Vacuum Plate of Aluminum and Aluminum Foil Using UV Offset Printing

2012 ◽  
Vol 262 ◽  
pp. 253-257
Author(s):  
Chang Lang Chen ◽  
Mei Chun Lo ◽  
Ming Chw Wei ◽  
Shi Wei Wang
Keyword(s):  
Aluminum Foil ◽  
Packaging Materials ◽  
Offset Printing ◽  
Color Gamut ◽  
Test Form ◽  
White Board ◽  
Printing Quality ◽  
Dot Gain ◽  
Green Packaging ◽  
The Difference

In packaging market, Vacuum Plate of Aluminum by UV offset printing is light impediment effectively, moisture-proof, and forgery-proof and reaches the request of the green packaging production. Thus, the packaging of Vacuum Plate of Aluminum is used widely. This study was a true experimental research. The test form employed was the printing quality control strip from international printing specification. It was used to record and analyze the color variance between Vacuum Plate of Aluminum and Aluminum Foil with white board and no white board using UV Offset Printing. The study indicated the comparison of Tone Value Increased & the difference of Color Gamut Range among these three packaging materials via C, M, Y, K four colors tone value and RGB area testing, and results reveals that the tone value on the Vacuum Plate of Aluminum with white board increased most among these three materials; the dot gain percentage of the Vacuum Plate of Aluminum is less than 15% which with the highest stability than others. All signature findings are given in Fig.1 and Fig.2. These results are entirely consistent with research hypotheses.

scholarly journals Extending and Matching a High Dynamic Range Image from a Single Image

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3950
Author(s):  
Van Luan Tran ◽  
Huei-Yung Lin
Keyword(s):  
Image Quality Assessment ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Performance Comparison ◽  
Range Image ◽  
High Dynamic Range Image ◽  
High Dynamic ◽  
The Difference ◽  
Imaging Algorithms ◽  
Low Dynamic Range

Extending the dynamic range can present much richer contrasts and physical information from the traditional low dynamic range (LDR) images. To tackle this, we propose a method to generate a high dynamic range image from a single LDR image. In addition, a technique for the matching between the histogram of a high dynamic range (HDR) image and the original image is introduced. To evaluate the results, we utilize the dynamic range for independent image quality assessment. It recognizes the difference in subtle brightness, which is a significant role in the assessment of novel lighting, rendering, and imaging algorithms. The results show that the picture quality is improved, and the contrast is adjusted. The performance comparison with other methods is carried out using the predicted visibility (HDR-VDP-2). Compared to the results obtained from other techniques, our extended HDR images can present a wider dynamic range with a large difference between light and dark areas.

scholarly journals Vertical Distribution of the Mirror Image Returns Observed by TRMM PR and Estimated for a 35-GHz Radar

2005 ◽  
Vol 22 (11) ◽  
pp. 1829-1837 ◽  
Author(s):  
Ji Li ◽  
Kenji Nakamura
Keyword(s):  
Vertical Distribution ◽  
Dynamic Range ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Attenuation ◽  
Mirror Image ◽  
Direct Image ◽  
Surface Reflection ◽  
Bistatic Scattering ◽  
Trmm Precipitation ◽  
The Difference

Abstract The vertical distribution of Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR)-observed precipitation reflectivity and their mirror image (MI) reflectivity are outlined in this paper. The purpose of this study is to investigate the possibility and the limitation of the MI method, which can be used to estimate rain attenuation. Because the MI returns are attenuated much more greatly than the direct image returns, and also because the MI return is affected by the surface reflection and surface scattering, the MI returns are much smaller and more complex than the direct image (DI) returns. However, because the MI returns might be contaminated by the surface or contributed by bistatic scattering near the surface, there are many strong mirror returns between the surface and below surface at 1 km. The ratio of detectable MI return pixels to detected DI return pixels depends on rain rate, target height, and storm height. In addition, differences also exist between the convective and stratiform rain. The reason for this might be because the difference of the surface cross section and the difference of the storm height between the two types of rainfall. Furthermore, the direct and the mirror returns for a 35-GHz radar are also estimated. The virtue of the MI method of the Ka-band radar may reside in expanding the dynamic range of the MI method from 4–30 to 0.6–30 mm h−1.

scholarly journals Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions

2018 ◽  
Vol 10 (11) ◽  
pp. 1772 ◽  
Author(s):  
Estrella Olmedo ◽  
Carolina Gabarró ◽  
Verónica González-Gambau ◽  
Justino Martínez ◽  
Joaquim Ballabrera-Poy ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Remotely Sensed ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
In Situ Data ◽  
Arctic Regions ◽  
The Difference

This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans ( 50 ∘ N– 90 ∘ N). The SMOS SSS maps presented in this work are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models.

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