- 标题
- 摘要
- 关键词
- 实验方案
- 产品
过滤筛选
- 2018
- LED stage lighting
- CRI
- RGBW four-color mixing model
- the blackbody locus fitting
- Optoelectronic Information Science and Engineering
- Communication University of China
-
Green synthesis of multi-color emissive carbon dots from Manilkara zapota fruits for bioimaging of bacterial and fungal cells
摘要： Natural resources have widely been used as precursors for the preparation of ultra-small carbon dots (C-dots) due to ease of availability, low cost and C-dots with high quantum yields (QYs). Herein, water dispersible multi-color emissive C-dots were obtained from Manilkara zapota fruits. The emission of C-dots was well tuned by sulphuric acid and phosphoric acids, which results to generate blue-, green- and yellow- C-dots. The fabricated C-dots exhibit blue, green and yellow color emissions when irradiated them under UV light at 365 nm. The emission/excitation peaks of blue-, green-, and yellow- C-dots were observed at 443, 515 and 563 nm when excited at 350, 420 and 440 nm, respectively. The QYs of blue-, green-, and yellow- C-dots are 5.7, 7.9 and 5.2 %. The average sizes of blue- green- and yellow- C-dots are 1.9±0.3, 2.9±0.7and 4.5±1.25 nm, respectively. Because of ultra-small size and biocompatibility, three C-dots act as promising bioimaging agents for imaging of cells (E. coli, Aspergillus aculeatus and Fomitopsis sp). The cytotoxicity on HeLa cells indicates that three C-dots have non-toxic nature, which confirms their biocompatibility. The ultra-small C-dots were effectively distributed in the cytoplasm of the cells, ensuring the potential applications in cell imaging and biomedical studies.
关键词： Multi-color emission,Manilkara zapota fruits,Bioimaging,Fluorescent C-dots
更新于2025-09-23 15:22:29
-
DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs
摘要： In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming participation of expert practitioners. Although several automated deep learning systems have been developed for classifying color fundus photographs (CFP) of individual eyes by AREDS severity score, none to date has used a patient-based scoring system that uses images from both eyes to assign a severity score. Design: DeepSeeNet, a deep learning model, was developed to classify patients automatically by the AREDS Simplified Severity Scale (score 0e5) using bilateral CFP. Participants: DeepSeeNet was trained on 58 402 and tested on 900 images from the longitudinal follow-up of 4549 participants from AREDS. Gold standard labels were obtained using reading center grades. Methods: DeepSeeNet simulates the human grading process by first detecting individual AMD risk factors (drusen size, pigmentary abnormalities) for each eye and then calculating a patient-based AMD severity score using the AREDS Simplified Severity Scale. Main Outcome Measures: Overall accuracy, specificity, sensitivity, Cohen’s kappa, and area under the curve (AUC). The performance of DeepSeeNet was compared with that of retinal specialists. Results: DeepSeeNet performed better on patient-based classification (accuracy ? 0.671; kappa ? 0.558) than retinal specialists (accuracy ? 0.599; kappa ? 0.467) with high AUC in the detection of large drusen (0.94), pigmentary abnormalities (0.93), and late AMD (0.97). DeepSeeNet also outperformed retinal specialists in the detection of large drusen (accuracy 0.742 vs. 0.696; kappa 0.601 vs. 0.517) and pigmentary abnormalities (accuracy 0.890 vs. 0.813; kappa 0.723 vs. 0.535) but showed lower performance in the detection of late AMD (accuracy 0.967 vs. 0.973; kappa 0.663 vs. 0.754). Conclusions: By simulating the human grading process, DeepSeeNet demonstrated high accuracy with increased transparency in the automated assignment of individual patients to AMD risk categories based on the AREDS Simplified Severity Scale. These results highlight the potential of deep learning to assist and enhance clinical decision-making in patients with AMD, such as early AMD detection and risk prediction for developing late AMD. DeepSeeNet is publicly available on https://github.com/ncbi-nlp/DeepSeeNet.
关键词： deep learning,age-related macular degeneration,automated classification,AREDS Simplified Severity Scale,color fundus photographs
更新于2025-09-23 15:22:29
-
An Approximate Schur Decomposition-based Spatial Domain Color Image Watermarking Method
摘要： In this paper, an approximate Schur decomposition-based spatial domain blind color image watermarking method is proposed to protect the copyright of color images, which has low computation complexity as same as the watermarking technique in the spatial domain and strong robustness as same as the watermarking technique in the transform domain. Firstly, the approximate maximum eigenvalue of Schur decomposition is calculated in the spatial domain by the proposed method. Secondly, the approximate maximum eigenvalue is used to embed and extract the color watermark image in the spatial domain without of the true Schur decomposition. Moreover, the procedures of the proposed watermarking method are given in details. The proposed technique is performed on the spatial domain based on the approximate Schur decomposition and belongs to blind watermarking technique. Experimental results on two publicly available image databases (CVG-UGR and USC-SIPI) have demonstrated the effectiveness of the proposed method in terms of invisibility, robustness, and the real-time feature.
关键词： Schur Decomposition,Color Image Watermark,Spatial Domain,Real-time Feature
更新于2025-09-23 15:22:29
-
[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Learning Illuminant Estimation from Object Recognition
摘要： In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, this is the first example of a deep learning architecture for illuminant estimation that is trained without ground truth illuminants. We evaluate our solution on standard datasets for color constancy, and compare it with state of the art methods. Our proposal is shown to outperform most deep learning methods in a cross-dataset evaluation setup, and to present competitive results in a comparison with parametric solutions.
关键词： Illuminant estimation,deep learning,convolutional neural networks,computational color constancy,semi-supervised learning
更新于2025-09-23 15:22:29
-
RGB color coded images in scanning electron microscopy of biological surfaces
摘要： We present here a methodological approach for the creation of color images in scanning electron microscopy by processing grayscale images taken simultaneously from at least three different detectors in a scanning electron microscope. The final color images are then produced by merging together those grayscale images in RGB color space. We show the images from non-conductive standard sample together with those obtained from real microbiological samples. The first one represents a microbial biofilm naturally grown on fiber glass filter. The other shows individual Bacillus subtilis cells from batch culture. All the image handling was done in open source image processing software ImageJ or GNU Image Manipulation Program (Gimp) or, alternatively, in proprietary AnalySis 3.2 Pro software processing suite.
关键词： high resolution,color images,scanning electron microscopy,biological surfaces
更新于2025-09-23 15:22:29
-
How qualitative spectral information can improve soil profile classification?
摘要： Soil classification is important to organize the knowledge of soil characteristics. Spectroscopy has increased in the last years as a technique for descriptive and quantitative evaluation of soils. Thus, our objective was to assess qualitative and quantitative methods on soil classification, based on model profiles. Soils in different environments in the Roraima state, Brazil, were evaluated and represented by 16 profiles, providing 109 soil samples, which were analyzed for particle size distribution, chemical attributes and spectral measurement. Visible-near infrared spectra (350–2500 nm) of soil samples were interpreted in terms of intensity, shape and features. The soil color obtained using a spectroradiometer and a colorimeter, and by a soil expert was compared. Descriptive and qualitative analyses were performed for all spectra of the soil profile samples. The descriptive evaluations of the spectral curves from all horizons of the same profile were used to identify the diagnostic attributes and assign a profile to a taxonomic class. This was possible because spectra of samples had specific shapes, features and intensities that combined to present a specific signature. The Outil Statistique d’Aide à la Cartogénèse Automatique and cluster quantitative analyses could not correctly group similar soil classes and they still need to be improved in order to extract all the variability of the spectral data to discriminate soil classes. Soil color quantification by the Munsell system using both equipments showed greater R2 and lower error than that achieved by a soil expert, due to influences of subjectivity inherent in human assessments. Based on this specific case, it was clear that the automatic system may be more consistent than the pedologist’s visual method. Future studies should focus on the development of an online tool that integrates a descriptive approach and spectral information of a given soil profile to determine its probable taxonomic class.
关键词： Munsell color system,soil classification,expert,NIR,colorimeter,spectroradiometer
更新于2025-09-23 15:22:29
-
Color Noise Reduction Method in Non-constant Luminance Signal for High Dynamic Range Video Service
摘要： A high dynamic range (HDR) video service is an upcoming industry. For compatibility with legacy devices receiving a non-constant luminance (NCL) signal, new tools supporting an HDR video service are required. The current pre-processing chain of HDR video can produce color noise owing to the chroma component down-sampling process for video encoding. Although a luma adjustment method has been proposed to solve this problem, some disadvantages still remain. In this paper, we present an adaptive color noise reduction method for an NCL signal of an HDR video service. The proposed method adjusts the luma component of an NCL signal adaptively according to the information of the luma component from a constant luminance signal and the level of color saturation. Experiment results show that the color noise problem is resolved by applying our proposed method. In addition, the speed of the pre-processing is increased more than two-fold compared to a previous method.
关键词： non-constant luminance,high efficiency video coding,wide color gamut,High dynamic range
更新于2025-09-23 15:22:29
-
QRCI: A new quantum representation model of color digital images
摘要： In this paper, a new quantum representation model of color digital images (QRCI) is investigated, in which the color information is encoded by the basis states of qubit sequences. QRCI model utilizes 2n+6 qubits to store a color digital image with size 2n×2n. Compared with the existing NCQI representation model, the storage capacity of QRCI improves 218 times. Moreover, some quantum color image processing operations concerning channels and bit-planes based on QRCI are discussed and their quantum circuits are designed. Comparison results of the quantum circuits indicate that these operations based on QRCI have lower quantum cost than NCQI. Therefore, the new proposed QRCI representation model can save more storage space and it is more convenient to conduct quantum color image processing operations concerning channels and bit-planes. This work will help the researchers to further investigate more complex quantum color image processing operations based on QRCI.
关键词： Quantum computation,Quantum cost,Quantum image representation model,Quantum color image processing,Storage capacity,Qubit
更新于2025-09-23 15:22:29
-
Ag-location-based color-tunable fluorescent AuAg nanoclusters for “turn-on” and “turn-off” detection of l-cysteine
摘要： The color-tunable AuAg nanoclusters (AuAg NCs) are designed by adjusting the location of Ag element, in which β-lactoglobulin (β-Lg) serves as the capping and reducing agent. The mono-metal Au NCs emit red-fluorescent emission (red-Au NCs@β-Lg); however, the stronger metallophilic interaction between Au and Ag facilitate the smaller size, and then the bimetal AuAg NCs@β-Lg present yellow emission (yellow-AuAg NCs@β-Lg). A "Ag+ shell" outside the AuAg NCs@β-Lg cause the emission change to orange (orange-AuAg NCs@β-Lg-Ag+). This is the first time for Ag+ to tune continuously the emission wavelength of AuAg NCs. TEM proves that the size of particle has a close relationship with the fluorescence emissions. L-cysteine (Cys) stabilized the protective effect of β-Lg and shows a "turn on" effect on the fluorescence intensity of yellow-AuAg NCs@β-Lg. In contrary, the strong interaction between Cys and Ag+ destroys the protection of β-Lg to the core of yellow-AuAg NCs@β-Lg and shows a distinct "turn off" effect on the fluorescence intensity of orange-AuAg NCs@β-Lg-Ag+. The above phenomenon has been successfully applied to the detection of Cys in the Hela cells. This location-based color-tunable strategy is expected to open up a new potential to improve the performance of NCs.
关键词： L-cysteine,Color-tunable,Ag-location,AuAg nanoclusters
更新于2025-09-23 15:22:29
-
Joint Color Space GMMs for CFA Demosaicking
摘要： We propose a patch-based algorithm for demosaicking a mosaicked color image produced by color filter arrays commonly used in acquiring color images. The proposed algorithm exploits a joint color space Gaussian mixture model (JCS-GMM) prior for jointly characterizing the patches from red, green, and blue channels of a color image. The inter channel correlations captured by the covariance matrices of Gaussian models are exploited to estimate the pixel values missing in the mosaicked image. The proposed JCS-GMM demosaicking algorithm can be seen as the GMM analogue of the Color-KSVD algorithm, which has produced impressive results in color image denoising and demosaicking. We demonstrate that our proposed algorithm achieves superior performance in the case of Kodak and Laurent Condat’s databases, and competitive performance in the case of IMAX database, when compared with state-of-the-art demosaicking algorithms.
关键词： Color filter array,demosaicking,Bayer pattern,Gaussian mixture models
更新于2025-09-23 15:22:29