Trichromatic Image Research Articles

Rational design of far red to near-infrared rhodamine analogues with huge Stokes shifts for single-laser excitation multicolor imaging

Rhodamine dyes have been widely employed in biological imaging and sensing. However, it is always a challenge to design rhodamine derivatives with huge Stokes shift to address the draconian requirements of single-excitation multicolor imaging. In this work, we described a generally strategy to enhance the Stokes shift of rhodamine dyes by completely breaking their electronic symmetry. As a result, the Stokes shift of novel rhodamine dye DQF-RB-Cl is up to 205 nm in PBS, which is the largest in all the reported rhodamine derivatives. In addition, we successfully realized the single excitation trichromatic imaging of mitochondria, lysosomes and cell membranes by combining DQF-RB-Cl with commercial lysosomal targeting probe Lyso-Tracker Green and membrane targeting dye Dil. This is the organic synthetic dyes for SLE-trichromatic imaging in cells for the first time. These results demonstrate the potential of our design as a useful strategy to develop huge Stokes shift fluorophore for bioimaging.

Prokaryote playhouse: A low-cost, laser-cut acrylic incubator for optogenetic bacterial culture

Bacterial photography is a printing technique that replaces conventional photochemistry with a living film of engineered Escherichia coli. Many biology teaching labs have adopted monochrome bacterial photography because it offers a captivating playground for illustrating central concepts and lab techniques in biological engineering, particularly in the fields of synthetic biology and optogenetics. Recent improvements have increased the number of color channels from one to three. A key practical challenge in three-color printing is to expose a Petri dish loaded with engineered bacteria to a trichromatic image while maintaining it at 37 °C. Prokaryote Playhouse is a compact, inexpensive, open-source, benchtop incubator for light-sensitive bacterial cultures that makes bacterial photography and similar bacterial optogenetic methods more accessible to teaching labs, makerspaces, and research labs. The system includes a laser-cut, light-tight enclosure; digital thermostat; heated sample shelf; single-board computer; and miniature projector. We built a fleet of Prokaryote Playhouses that students have used to produce hundreds of bacterial photographs in a wide range of educational experiences, ranging from a four-hour introduction to synthetic biology and wet lab techniques to a six-week exploratory class for first-year students at MIT.

Spectral radiance reconstruction from trichromatic camera responses based on orthogonal test and regularized algorithm

AbstractReconstruction of spectral information based on multi‐channel image system is a significant problem in color reproduction, detection, and recognition. A spectral radiance reconstruction from trichromatic digital camera responses is researched in this article. The mapping relationship between the trichromatic imaging system response and the incident spectral radiance is analyzed. Then, in order to remove the ill‐posedness of the problem, a regularized constraint solution model of spectral radiance reconstruction matrix is established. And the spectral radiance can be reconstructed by spectral radiance reconstruction matrices and trichromatic imaging system response. Finally, the spectral radiance reconstruction matrix is estimated by the system radiometric calibration experiment. The input radiance is offered by a LCD display. A 3‐factor and 9‐level orthogonal test is designed for the calibration experiment, and a test set of 24 colors is used for precision analysis. The results show that the average relative mean error of our method is 8.69%, it is lower than that of Wiener filtering method by 2.84%. The method can reconstruct spectral radiance information effectively.

Edge detection of heterogeneity in transmission images based on frame accumulation and multiband information fusion

In optical transmission imaging, even a very thin layer of a scattering material can appear opaque make the image blurry, which restricts the positioning of heterogeneity in transmission images. Aiming at the problem, an exploratory method based on frame accumulation and multiband information fusion was proposed and proven, which can be used to detect the edge of heterogeneity in transmission images. Treating the trichromatic image as an example, the following experiment was designed: Firstly, the transmission video of phantom is collected by camera. Then, single-band images are decoded at “R∖G∖B” and further accumulated by frame accumulation technology to obtain the image with high grayscale level, respectively. Lastly, the information fusion of the three single-band images is realized through neural network, and further the edge could be detected by the established model. The experimental result shows that the heterogeneity edge can be detected more accurately by this method. Therefore, the validity of this method for detecting heterogeneity in transmission images is demonstrated. It is expected to provide an idea for the future clinical diagnosis of breast tumor by transmission method.

Contribution of color to material perception

Color vision enables us to discriminate objects based on the wavelength composition of reflected light, but this is not all about the functional contributions of color to visual recognition. Since a trichromatic image carries more information about the optical properties of a scene than a monochromatic image does, color could help us recognize complex material qualities. For instance, color vision assists our gloss perception. For glossy dielectric materials, the spectral distribution of illumination is preserved in the specular reflection, while modulated by surface light absorption for the diffuse reflection. When this physical constraint holds (e.g., red highlights on red body, white highlights on red body), human observers perceive naturalistic glossy surfaces. However, when the constraint is violated (e.g., red highlights on white body, red highlights on green body), the highlights look unnatural (Nishida et al., VSS 2008, 2011). We also found that human vision uses color statistics of an image for the perception of an ecologically important surface condition, i.e., wetness (Sawayama, Adelson & Nishida, 2017, Journal of Vision). Psychophysical experiments showed that overall enhancement of chromatic saturation, combined with a luminance tone change that increases the darkness and glossiness of the image, tends to effectively make dry scenes look wetter. Theoretical analysis along with image analysis of real objects indicated that our image transformation, which we call the wetness enhancing transformation (WET), is consistent with actual optical changes produced by surface wetting.

Visual wetness perception based on image color statistics

Color vision provides humans and animals with the abilities to discriminate colors based on the wavelength composition of light and to determine the location and identity of objects of interest in cluttered scenes (e.g., ripe fruit among foliage). However, we argue that color vision can inform us about much more than color alone. Since a trichromatic image carries more information about the optical properties of a scene than a monochromatic image does, color can help us recognize complex material qualities. Here we show that human vision uses color statistics of an image for the perception of an ecologically important surface condition (i.e., wetness). Psychophysical experiments showed that overall enhancement of chromatic saturation, combined with a luminance tone change that increases the darkness and glossiness of the image, tended to make dry scenes look wetter. Theoretical analysis along with image analysis of real objects indicated that our image transformation, which we call the wetness enhancing transformation, is consistent with actual optical changes produced by surface wetting. Furthermore, we found that the wetness enhancing transformation operator was more effective for the images with many colors (large hue entropy) than for those with few colors (small hue entropy). The hue entropy may be used to separate surface wetness from other surface states having similar optical properties. While surface wetness and surface color might seem to be independent, there are higher order color statistics that can influence wetness judgments, in accord with the ecological statistics. The present findings indicate that the visual system uses color image statistics in an elegant way to help estimate the complex physical status of a scene.

Information-Theoretic Modeling of Trichromacy Coding of Light Spectrum

Trichromacy is the representation of a light spectrum by three scalar coordinates. Such representation is universally implemented by the human visual system and by RGB (Red Green Blue) cameras. We propose here an informational model for trichromacy. Based on a statistical analysis of the dynamics of individual photons, the model demonstrates a possibility for describing trichromacy as an information channel, for which the input–output mutual information can be computed to serve as a measure of performance. The capabilities and significance of the informational model are illustrated and motivated in various situations. The model especially enables an assessment of the influence of the spectral sensitivities of the three types of photodetectors realizing the trichromatic representation. It provides a criterion to optimize possibly adjustable parameters of the spectral sensitivities such as their center wavelength, spectral width or magnitude. The model shows, for instance, the usefulness of some overlap with smooth graded spectral sensitivities, as observed for instance in the human retina. The approach also, starting from hyperspectral images with high spectral resolution measured in the laboratory, can be used to devise low-cost trichromatic imaging systems optimized for observation of specific spectral signatures. This is illustrated with an example from plant science, and demonstrates a potential of application especially to life sciences. The approach particularizes connections between physics, biophysics and information theory.

Segmentation and estimation of spatially varying illumination.

In this paper, we present an unsupervised method for segmenting the illuminant regions and estimating the illumination power spectrum from a single image of a scene lit by multiple light sources. Here, illuminant region segmentation is cast as a probabilistic clustering problem in the image spectral radiance space. We formulate the problem in an optimization setting, which aims to maximize the likelihood of the image radiance with respect to a mixture model while enforcing a spatial smoothness constraint on the illuminant spectrum. We initialize the sample pixel set under each illuminant via a projection of the image radiance spectra onto a low-dimensional subspace spanned by a randomly chosen subset of spectra. Subsequently, we optimize the objective function in a coordinate-ascent manner by updating the weights of the mixture components, sample pixel set under each illuminant, and illuminant posterior probabilities. We then estimate the illuminant power spectrum per pixel making use of these posterior probabilities. We compare our method with a number of alternatives for the tasks of illumination region segmentation, illumination color estimation, and color correction. Our experiments show the effectiveness of our method as applied to one hyperspectral and three trichromatic image data sets.

Linear Modelling for Spectral Images Based on Truncated Fourier Series

Reflectance spectra of hyperspectral images of the natural scenes are supposed to represent the real world better than any certain classes of natural and man-made spectral reflectance. But spectral images contain a large volume of data and place considerable demands on computer hardware and software compared with standard trichromatic image storage and processing. Although principal component analysis (PCA) based low-dimensional linear models have been widely used in spectral image encoding and compression, there is no a single PCA linear model derived from one data set can be guaranteed to accurate represent other data sets. In this study, we proposed a spectral image encoding method by a single linear model constructed by truncated Fourier series, in which a limited number of parameters that is proportional to the highest frequency cut off if the low-pass hypothesis is valid for any of the data sets. In this paper, several groups of hyperspectral images have been processed using truncated Fourier series, the encoded image are analysed in terms of chromaticity and spectral root mean square (RMS) errors. Results show spectral images can be efficiently compressed when the frequency reach certain limit, and the color information can be well preserved, but there are also large variations across different data sets. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3275

On the Information Content along Edges in Trichromatic Images

On the Information Content along Edges in Trichromatic Images

License plate location based on trichromatic imaging and color-discrete characteristic

License plate location (LPL) is the key part of automatic license plate recognition (LPR) system that plays an important role in many applications and a number of LPL methods based on color information have been proposed. However, some problems, such as country specific, similar color interference, lighting variation sensitive, time consuming, processing only one kind of LP with same color combination a time and omitting the case of more than one combinations in a LP, should be solved further. In this study, a color-based LPL method that consists of three modules: color edge extraction, denoising and searching, is proposed. Color edge extraction, the kernel of the proposed, is designed by color-discrete characteristics of license plates in the trichromatic wavebands. In the experiment, 1384 images taken from natural scenes in China and other 104 countries or regions are employed. Of which, 74 have been failed to locate the license plates. The success rate and average execution time are 94.7% and 57ms, respectively.

The Perceptual Study of the Tolerance of Spectral Images Based on Bootstrap Analysis

Spectral images contain a large volume of data and place considerable demands on computer hardware and software compared with standard trichromatic image storage and processing. Although the imformation of reflectance spectra may be represented efficiently using linear modelsa key task is to answer how many basis functions of a linear model are necessary for a given accuracy of representation. Because the most common application of spectral images is the reproduction of color and images, it ishighlyimportant to study the tolerance of human perceptions over spectral images represented by low-dimensional linear models. In this study several data sets and psychophysical studies have been used to investigate how many basis functions are necessary to represent a spectral image to be indistinguishable from the original image. The raw objective experiment datas are analysed by a comprehensive probit analysis based on the bootstrap method, and results show that the perceptual tolerance for spectral images reconsctucted by variouse number of basis functions is highly image dependent.

Graying for images with color-discrete characteristic

It is realized that there is a category of images with color-discrete characteristic, widely investigated in the fields of machine vision, image processing and pattern recognition. Despite the authority of weighted-average method on graying, it is not robust enough in the preceding case. In this paper, a graying method based on trichromatic imaging and color-discrete characteristic is proposed. Detailed experimental evidence has demonstrated that this method can maximize contrast of target region furthest while graying; also this feature can improve performance of thresholding and image enhancement, which has significant effect on image processing tasks in natural scenes. In addition, the proposed may help to optimize design of imaging devices.

A Novel Method for Representation of Spectral Images Based on Color Matching Functions

Spectral images contain a large volume of data and the development of multispectral imaging systems places considerable demands on computer hardware and software compared with standard three-component or trichromatic image storage and processing. This study is concerned with lossy compression techniques for spectral images since many color images are intended for display for human perception and it is well established that images contain redundancies (in terms of their color, spatial and temporal properties) that can be removed without any loss in image quality. The lossy compression technique that is considered in this work is a low-dimensional linear model of spectral reflectance, with which the basis function are derived from color matching functions that are correlated with human visual system.

Imaging fingerprinting of excitation emission matrices

The spectral fingerprinting of the excitation emission matrix (EEM) of fluorescent substances is demonstrated using polychromatic light sources and tri-chromatic image detectors. A model of the measured fingerprints explaining their features and classification performance, based on the polychromatic excitation of the indicators is proposed. Substantial amount of spectral information is retained in the fingerprints as corroborated by multivariate analysis and experimental conditions that favor such situation are identified. In average, for five different substances, the model shows a fitting goodness measured by the Pearson's r coefficient and the root mean square deviation of 0.8541 and 0.0247 respectively, while principal component classification patterns satisfactorily compare with the EEM spectroscopy classification and respectively explain 96% and 93% of the information in the fist two principal components. The measurements can be performed using regular computer screens as illumination and web cameras as detectors, which constitute ubiquitous and affordable platforms compatible with distributed evaluations, in contrast to regular instrumentation for EEM measurements.

Applying <italic>LabRGB</italic> to Real Multi-Spectral Images

A spectral encoding/decoding method, called LabRGB, was proposed last year [1]. LabRGB consists of six unique base functions and physically meaningful encoding values. LabRGB has several features; color characteristics can be estimated by its encoding values, fixed base functions need not exchange beforehand and tri-chromatic images as well as multi-spectral images can be handled by a single form. In this paper, the derivation of LabRGB was described in detail including fast straightforward calculation of encoding/decoding calculation. Then LabRGB was applied to real multi-spectral images. LabRGB performed well in encoding/decoding various types of multi-spectral images and confirmed to be suitable for practical use.

Spectral Encoding/Decoding Using LabRGB

Spectral encoding/decoding methods using unique base functions and physically meaningful values were explored. Three new methods such as, TrW6 consisting of six unique trigonometric functions, Lab2 consisting of two CIELAB functions, and LabRGB consisting of CIELAB and RGB, were derived and compared against the traditional eigenvectors method. It was found that TrW6 and LabRGB showed almost the same accuracy as the traditional eigenvector method. By using LabRGB, color characteristics can be estimated by only looking at its encoding values and we do not have to exchange base functions beforehand for exchanging a different population of object colors. LabRGB can be applied not only to spectral imaging but also to traditional trichromatic imaging world, so its use can extend beyond spectral uses.

Spectral encoding / decoding using <italic>LabRGB</italic>

Spectral encoding / decoding methods using unique eigenvectors and physically meaningful values were explored. Three new methods such as, TrW6 consisting of 6 unique trigonometric functions, Lab2 consisting of two CIELAB, LabRGB consisting of CIELAB and RGB, were derived and compared against the traditional eigenvectors method. It was found that TrW6 and LabRGB showed almost the same accuracy as the traditional eigenvectors method. By using LabRGB, color characteristics can be estimated by only looking at its encoding signals and we do not have to exchange eigenvectors beforehand for exchanging a different population of object colors. LabRGB can be applied not only to spectral imaging but also to traditional tri-chromatic imaging world, so its usage is unlimited.

Color opponency is an efficient representation of spectral properties in natural scenes

The human visual system encodes the chromatic signals conveyed by the three types of retinal cone photoreceptors in an opponent fashion. This opponency is thought to reduce redundant information by decorrelating the photoreceptor signals. Correlations in the receptor signals are caused by the substantial overlap of the spectral sensitivities of the receptors, but it is not clear to what extent the properties of natural spectra contribute to the correlations. To investigate the influences of natural spectra and photoreceptor spectral sensitivities, we attempted to find linear codes with minimal redundancy for trichromatic images assuming human cone spectral sensitivities, or hypothetical non-overlapping cone sensitivities, respectively. The resulting properties of basis functions are similar in both cases. They are non-orthogonal, show strong opponency along an achromatic direction (luminance edges) and along chromatic directions, and they achieve a highly efficient encoding of natural chromatic signals. Thus, color opponency arises for the encoding of human cone signals, i.e. with strongly overlapping spectral sensitivities, but also under the assumption of non-overlapping spectral sensitivities. Our results suggest that color opponency may in part be a result of the properties of natural spectra and not solely a consequence of the cone spectral sensitivities.

The Dense Plasma Torus around the Nucleus of an Active Galaxy NGC1052

Abstract A subparsec-scale dense plasma torus around an active galactic nucleus is unveiled. We report on very-long-baseline interferometry (VLBI) observations at 2.3, 8.4, and 15.4 GHz towards the active galaxy NGC1052. The convex spectra of the double-sided jets and the nucleus imply that synchrotron emission is obscured through free–free absorption (FFA) by the foreground cold dense plasma. A trichromatic image was produced to illustrate the distribution of the FFA opacity. We found a central condensation of the plasma which covers about 0.1 pc and 0.7 pc of the approaching and receding jets, respectively. A simple explanation for the asymmetric distribution is the existence of a thick plasma torus perpendicular to the jets. We also found an ambient FFA absorber, whose density profile can be ascribed to a spherical distribution of the isothermal King model. The coexistence of torus-like and spherical distributions of the plasma suggests a transition from radial accretion to rotational accretion around the nucleus.