Accurate Color Research Articles

A Review of Methods of Removing Haze from An Image

A literature review aids in comprehending and gaining further information about a certain area of a subject. The presence of haze, fog, smoke, rain, and other harsh weather conditions affects outdoor photos. Images taken in unnatural weather have weak contrast and poor colors. This may make detecting objects in the produced hazy pictures difficult. In computer vision, scenes and images taken in a foggy atmosphere suffer from blurring. This work covers a study of many remove haze algorithms for eliminating haze collected in real-world weather scenarios in order to recover haze-free images rapidly and with improved quality. The contrast, viewing range, and color accuracy have been enhanced. All of these techniques it is used in countless fields. Some of the applications that use this technology outdoor surveillance, object recognition, underwater photography, and so on.

Rapid full-color Fourier ptychographic microscopy via spatially filtered color transfer

Full-color imaging is of critical importance in digital pathology for analyzing labeled tissue sections. In our previous cover story [Sci. China: Phys., Mech. Astron. 64, 114211 (2021)SCPMCL1674-734810.1007/s11433-021-1730-x], a color transfer approach was implemented on Fourier ptychographic microscopy (FPM) for achieving high-throughput full-color whole slide imaging without mechanical scanning. The approach was able to reduce both acquisition and reconstruction time of FPM by three-fold with negligible trade-off on color accuracy. However, the method cannot properly stain samples with two or more dyes due to the lack of spatial constraints in the color transfer process. It also requires a high computation cost in histogram matching of individual patches. Here we report a modified full-color imaging algorithm for FPM, termed color-transfer filtering FPM (CFFPM). In CFFPM, we replace the original histogram matching process with a combination of block processing and trilateral spatial filtering. The former step reduces the search of the solution space for colorization, and the latter introduces spatial constraints that match the low-resolution measurement. We further adopt an iterative process to refine the results. We show that this method can perform accurate and fast color transfer for various specimens, including those with multiple stains. The statistical results of 26 samples show that the average root mean square error is only 1.26% higher than that of the red-green-blue sequential acquisition method. For some cases, CFFPM outperforms the sequential method because of the coherent artifacts introduced by dust particles. The reported CFFPM strategy provides a turnkey solution for digital pathology via computational optical imaging.

Association Patterns among Physical, Chemical and Microbiological Indicators of Springs in Rio Grande do Sul, Brazil

This study aimed to verify the linear associations between the physical, chemical and microbiological variables of spring water. The research was developed from two seasons of spring water sample collections and evaluated physical–chemical variables such as temperature, pH, turbidity, electrical conductivity, total alkalinity, total hardness, total ammonia, nitrite, nitrate, true and apparent colors, total phosphorus, fluoride and total iron, and microbiological variables—total coliforms and Escherichia coli. The variables’ total alkalinity, total hardness, and electrical conductivity have a strong positive correlation among them (r > 0.50), which is similar to what occurs with the variables’ turbidity, apparent color, true color, and total iron, between nitrite and total iron, and between the turbidity and total coliforms. These correlations occur as a function of the interaction that water has with the soil and the compounds found, thus altering the quality. The springs do not have masonry protection, they only have plant protection. Moreover, this water is exposed and accessible to animals in these areas. In addition, the result of the surface and sub-surface flow effect of spring water must be considered.

Convolutional Neural Networks Quantization with Double-Stage Squeeze-and-Threshold.

It has been proven that, compared to using 32-bit floating-point numbers in the training phase, Deep Convolutional Neural Networks (DCNNs) can operate with low-precision during inference, thereby saving memory footprint and power consumption. However, neural network quantization is always accompanied by accuracy degradation. Here, we propose a quantization method called double-stage Squeeze-and-Threshold (double-stage ST) to close the accuracy gap with full-precision models. While accurate colors in pictures can be pleasing to the viewer, they are not necessary for distinguishing objects. The era of black and white television proves this idea. As long as the limited colors are filled reasonably for different objects, the objects can be well identified and distinguished. Our method utilizes the attention mechanism to adjust the activations and learn the thresholds to distinguish objects (features). We then divide the numerically rich activations into intervals (a limited variety of numerical values) by the learned thresholds. The proposed method supports both binarization and multi-bit quantization. Our method achieves state-of-the-art results. In binarization, ReActNet [Z. Liu, Z. Shen, S. Li, K. Helwegen, D. Huang and K. Cheng, arXiv:abs/2106.11309] trained with our method outperforms the previous state-of-the-art result by 0.2 percentage points. Whereas in multi-bit quantization, the top-1 accuracy of the 3-bit ResNet-18 [K. He, X. Zhang, S. Ren and J. Sun, Deep residual learning for image recognition, 2016 IEEE Conf. Computer Vision and Pattern Recognition, CVPR 2016, 27-30 June 2016, Las Vegas, NV, USA (IEEE Computer Society, 2016), pp. 770-778] model exceeds the top-1 accuracy of its full-precision baseline model by 0.4 percentage points. The double-stage ST activation quantization method is easy to apply by inserting it before the convolution. Besides, the double-stage ST is detachable after training and introducing no computational cost in inference.

Dispersion-free highly accurate color recognition using excitonic 2D materials and machine learning

Dispersion is accepted as a fundamental step required for analyzing broadband light. The recognition of color by the human eye, its digital reproduction by a camera, or detailed analysis by a spectrometer all utilize dispersion; it is also an inherent component of color detection and machine vision. Here, we present a device (called artificial eye or, A-Eye) that accurately recognizes and reproduces tested colors, without any spectral dispersion. Instead, A-Eye uses N = 3–12 transmissive windows each with unique spectral features resulting from the broadband transmittance and excitonic peak-features of 2D transition metal dichalcogenides. Colored light passing through (and modified by) these windows and incident on a single photodetector generated different photocurrents, and these were used to create a reference database (training set) for 1337 “seen” and 0.55 million synthesized “unseen” colors. By “looking” at test colors modified by these windows, A-Eye can accurately recognize and reproduce “seen” colors with zero deviation from their original spectra and “unseen” colors with only ∼1 % median deviation, using the k-NN algorithm. A-Eye can continuously improve color estimation by adding any corrected guesses to its training database. A-Eye’s accurate color recognition dispels the notion that dispersion of colors is a prerequisite for color identification and paves the way for ultra-reliable color-recognition by machines with reduced engineering complexity.

Interlinguistic conflict: Word-word Stroop with first and second language colour words.

The congruency (or Stroop) effect is a standard observation of slower and less accurate colour identification to incongruent trials (e.g. "red" in green) relative to congruent trials (e.g. "red" in red). This effect has been observed in a word-word variant of the task, when both the distracter (e.g. "red") and target (e.g. "green") are colour words. The Stroop task has also been used to study the congruency effect between two languages in bilinguals. The typical finding is that the congruency effect for L1 words is larger than that for L2 words. For the first time, the present report aims to extend this finding to a word-word variant of the bilingual Stroop task. In two experiments, French monolinguals performed a bilingual word-word Stroop task in which target word language, language match, and congruency between the distracter and target were manipulated. The critical manipulation across two experiments concerned the target language. In Experiment 1, target language was manipulated between groups, with either French (L1) or English (L2) target colour words. In Experiment 2, target words from both languages were intermixed. In both experiments, the congruency effect was larger when the distracter and target were from the same language (language match) than when they were from different languages (language mismatch). Our findings suggested that this congruency effect mostly depends on the language match between the distracter and target, rather than on a target language. It also did not seem to matter whether the language-mismatching distracter was or was not a potential response alternative. Semantic activation of languages in bilinguals and its implications on target identification are discussed.

Preliminary color characterization of HP Multi Jet Fusion additive manufacturing with different orientations and surface finish

PurposeThis study aims to evaluate the color accuracy of HP Jet Fusion 580 3D printing, comparing 3D-printed outcomes against original digital input colors.Design/methodology/approachA custom cyan, magenta, yellow and black (CMYK) and red, green, blue (RGB) color chart was applied to the top, bottom and side surfaces of a 3D model. Four of each model were 3D-printed on a HP Jet Fusion 580, and half the samples were finished with a cyanoacrylate gloss surface finish, while half were left in raw form. A spectrophotometer was used to document CIELAB (L*a*b*) data, and comparisons made to the original input colors, including calculation of ΔE.FindingsThe CMYK samples were significantly more accurate than RGB samples, and grayscale samples in both color spaces were the most accurate of all. Typically, CMYK swatches were darker than the input values, and gloss samples were consistently darker than raw samples. The chromaticity (a*b*) range was found to be significantly smaller than what can be achieved digitally, with highly saturated colors unable to be produced by the printer.Originality/valueThis is the first study, to the best of the authors’ knowledge, to characterize the full color spectrum possible with the HP Jet Fusion 580, recommending that designers use the CMYK color space when applying colors and textures to 3D models. A quick-reference color chart has been provided; however, it is recommended that future research focus on developing a color management profile to better map digital colors to the capabilities of the printer.

Representing the true colors of Bai Xian-yong in the English language: “A Touch of Green” recounted

ABSTRACT Bai Xian-yong is one of the most important writers in contemporary Chinese and world literature. His masterpiece Taipei People, a collection of fourteen short stories, presents a gallery of Chinese mainland émigrés to Taiwan in 1949 in the wake of Chinese civil war. As a classic of Taiwanese modernism, Taipei People features an emotional sensitivity and a lingual novelty comparable to those of James Joyce’s Dubliners. Its English version was translated by the author and Patia Yasin, and edited by George Kao, the co-founder of Renditions. The paper attempts to compare and contrast the renowned short story, “A Touch of Green,” with its original Chinese, focusing on some sentences, phrases, proper nouns and the title itself, whose English renditions might not be considered tantamount to their original Chinese – a Chinese so crisply and creatively converging both classical beauty and colloquial flavor that only Bai Xian-yong the Chinese novelist knows how to craft. The research argues that if equivalent English rendition abiding by the principle of optimal fidelity exists as the promise land for the promotion of Taiwan literature across the world, future literary translators may need to adopt a multi-etymological strategy to represent the whole picture of Bai’s well-wrought works.

Nanosys CEO Jason Hartlove's Quest for Quantum Dot Supremacy

Nanosys CEO Jason Hartlove's Quest for Quantum Dot Supremacy

Color Barcode Detection of Complementary Color Barcode-based Optical Camera Communications with a Deep Neural Network

Complementary color barcode-based optical camera communications (CCB-OCC), which uses electronic displays and cameras as data transmitters and receivers, can offer a simple and short-range communications interface. In this paper, we enhance the data rate of a CCB-OCC scheme by using deep neural network (DNN)-based color barcode detection and adaptive color-value extraction. Unlike a conventional scheme, the proposed method extracts a color barcode region using a You Only Look Once (YOLO) real-time detection model that provides reliable barcode region extraction from various viewing angles between the display and the camera. In addition, the proposed scheme extracts accurate color values from color histograms using adaptive peak positions that express consecutive signals between packets. Experimental results verify data rate improvement in the proposed CCB-OCC scheme using DNN-based barcode detection and adaptive color-value extraction in a display-to-camera (D2C) communications link.

Automatic used mobile phone color determination: Enhancing the used mobile phone recycling in China

Rapid development of telecommunication technology in China has led to a prosperous market of smart phones, as well as an increase number of used phones. Nevertheless, there are key factors affecting the used phone recycling, one of which is the phone color. To realize an accurate automatic color recognition of used phones to enhance the recycling process, a high-dimensional spatial color conversion deep convolutional neural network (HSCCNet) is proposed in this paper. First, we established a common dataset for the field of used electronic devices. Second, the phone color is converted to the high-dimensional space of hue, saturation and value (HSV), which generates richer expressions of color features and improves the model sensitivity. Finally, a deep convolutional structure for HSV features is designed, where color feature conversion are implemented, resulting in enhanced color feature expressions. Promising results are obtained through the comparison between the proposed HSCCNet and the state-of-the-art models.

Back-compatible Color QR Codes for colorimetric applications

Color correction techniques in digital photography often rely on the use of color correction charts, which require including this relatively large object in the field of view. We propose here to use QR Codes to pack these color charts in a compact form factor, in a fully compatible manner with conventional black and white QR Codes; this is, without losing any of their easy location, sampling and digital data storage features. First, we present an algorithm to build these new colored QR Codes that preserves the original QR Code functionality - much more than other coloring proposals based on the random substitution of black and white pixels by colors - that relies on the ability of the native CRC code to correct and counteract these alterations. Second, we demonstrate that, as a result, these QR Codes can allocate far many more colors than the conventional color correction charts, enabling much more accurate color correction schemes in a more convenient and usable format.

A Novel and Cost-Effective CsVO3 Quantum Dots for Optoelectronic and Display Applications.

Quantum dots (QDs) have an unparalleled ability to mimic true colors due to their size-tunable optical and electronic properties, which make them the most promising nanoparticles in various fields. Currently, the majority of QDs available in the market are cadmium, indium, and lead-based materials but the toxicity and unstable nature of these QDs restricts their industrial and practical applications. To avoid using heavy metal ions, especially cadmium, the current research is focused on the fabrication of perovskite and vanadate QDs. Herein, we report the facile synthesis of a novel and cost-effective CsVO3 QDs for the first time. The sizes of the CsVO3 QDs produced were tuned from 2 to 10 nm by varying the reaction temperature from 140 to 190 °C. On increasing QD size, a continuous red shift was observed in absorption and emission spectra, signifying the presence of quantum confinement. In addition, along with CsVO3 QDs, the CsVO3 nanosheets self-assembled microflower-like particles were found as residue after the centrifugation; the X-ray diffraction indicated an orthorhombic structure. Under 365 nm excitation, these CsVO3 microflower-like particles exhibited broad emission with CIE coordinates in the white emission region. The acquired results suggest that CsVO3 QDs may represent a new class of cadmium-free materials for optoelectronic and biomedical applications.

International Anthems for LGBTQI+ Community: A Gay Pride– Thematic Analysis

Music as an art form of expression brings the inner side of an artist to the surface, and gives the world an insight of his vision. On the other hand, art is a conscious use of skill and creative imagination especially in the production of aesthetic objects or works. Music is a unique form of art and a lyrical and auditory representation of story. Thus, the study analyzed the patterns or themes of the five selected Gay Pride Playlists or songs such as I’m Coming Out, Born This Way, I Will Survive, True Colors, and Beautiful. The study found out three major themes and patterns that reflected in the five selected Gay Pride Playlists and entailed us to deepen our understanding about sexual orientation, and gender identity and expression. Furthermore, Gay Pride Playlists music, and music as an art, is made for expressing a free spirit that constitutes beauty.

Virtual Stain Transfer in Histology via Cascaded Deep Neural Networks

Pathological diagnosis relies on the visual inspection of histologically stained thin tissue specimens, where different types of stains are applied to bring contrast to and highlight various desired histological features. However, the destructive histochemical staining procedures are usually irreversible, making it very difficult to obtain multiple stains on the same tissue section. Here, we demonstrate a virtual stain transfer framework via a cascaded deep neural network (C-DNN) to digitally transform hematoxylin and eosin (H&E) stained tissue images into other types of histological stains. Unlike a single neural network structure that only takes one stain type as input to digitally output images of another stain type, C-DNN first uses virtual staining to transform autofluorescence microscopy images into H&E and then performs a stain transfer from H&E to the domain of the other stain in a cascaded manner. This cascaded structure in the training phase allows the model to directly exploit histochemically stained image data on both H&E and the target special stain of interest. This advantage alleviates the challenge of paired data acquisition and improves the image quality and color accuracy of the virtual stain transfer from H&E to another stain. We validated the superior performance of this C-DNN approach using kidney needle core biopsy tissue sections and successfully transferred the H&E stained tissue images into a virtual PAS (periodic acid-Schiff) stain. This method provides high-quality virtual images of special stains using existing, histochemically stained slides and creates new opportunities in digital pathology by performing highly accurate stain-to-stain transformations.

Sar2color: Learning Imaging Characteristics of SAR Images for SAR-to-Optical Transformation

Optical images are rich in spectral information, but difficult to acquire under all-weather conditions, while SAR images can overcome adverse meteorological conditions, but geometric distortion and speckle noise will reduce the quality of SAR images and thus make image interpretation more challenging. Therefore, transforming SAR images to optical images to assist SAR image interpretation will bring opportunities for SAR image application. With the advancement of deep learning technology, the ability of SAR-to-optical transformation has been greatly improved. However, most of the current mainstream transformation methods do not consider the imaging characteristics of SAR images, and there will be failures such as noisy color spots and regional landform deformation in the generated optical images. Moreover, since the SAR image itself does not contain color information, there also exist many color errors in these results. Aiming at the above problems, Sar2color, an end-to-end general SAR-to-optical transformation model, is proposed based on a conditional generative adversarial network (CGAN). The model uses DCT residual block to reduce the effect of coherent speckle noise on the generated optical images, and constructs the Light atrous spatial pyramid pooling (Light-ASPP) module to mitigate the negative effect of geometric distortion on the generation of optical images. These two designs ensure the precision of texture details when the SAR image is transformed into an optical image, and use the correct color memory block (CCMB) to improve the color accuracy of transformation results. Towards the Sar2color model, we have carried out evaluations on the homologous heterogeneous SAR image and optical image pairing dataset SEN1-2. The experimental results show that, compared with other mainstream transformation models, Sar2color achieves the state-of-the-art effect on all three objective and one subjective evaluation metrics. Furthermore, we have carried out various ablation experiments, and the results show the effectiveness of each designed module of Sar2color.

PolyU-BPCoMa: A dataset and benchmark towards mobile colorized mapping using a backpack multisensorial system

Constructing colorized point clouds from mobile laser scanning and images is a fundamental work in surveying and mapping. It is also an essential prerequisite for building digital twins for smart cities. However, existing public datasets are either in relatively small scales or lack accurate geometrical and color ground truth. This paper documents a multisensorial dataset named PolyU-BPCoMA which is distinctively positioned towards mobile colorized mapping. The dataset incorporates resources of 3D LiDAR, spherical imaging, GNSS and IMU on a backpack platform. Color checker boards are pasted in each surveyed area as targets and ground truth data are collected by an advanced terrestrial laser scanner (TLS). 3D geometrical and color information can be recovered in the colorized point clouds produced by the backpack system and the TLS, respectively. Accordingly, we provide an opportunity to benchmark the mapping and colorization accuracy simultaneously for a mobile multisensorial system. The dataset is approximately 800 GB in size covering both indoor and outdoor environments. The dataset and development kits are available at https://github.com/chenpengxin/PolyU-BPCoMa.git . • A dataset and benchmark towards mobile colorized mapping using a backpack multisensorial system. • An opportunity to benchmark the mapping and colorization accuracy simultaneously for MLS. • A development kit and a baseline result for performance comparison are available. • Other usages include LiDAR/visual odometry and terrestrial point cloud registration.

Developing a digital archive system for imperial Chinese robe in the Qing Dynasty.

The digital archive of cultural heritage provides new opportunities for the protection of the cultural heritage and the development of online museums. One of the essential requirements for the digitization is to achieve accurate color reproduction. Taking the Imperial Chinese robes in the Qing Dynasty as an example, this study aims to develop a digital achieve system to digitize the robes using a high-end imaging system and accurately reproduce their color properties on a display. Currently, there has been very limited study focused on the color reproduction of silk fabrics or other textile materials. The conventional color management process using a traditional color chart, however, may not be suitable for the reproduction of silk fabrics because they have very high gloss. To address this difficulty, a unique “Qianlong Palette” color chart, consisting of 210 silk fabric samples, has been specifically produced for optimizing the color reproduction of silk fabrics and a color image reproduction system has been developed for the digitization and archiving of the clothing fabric for the royal court. Color characterization models using both the “Qianlong Palette” color chart and the traditional color chart, and different mapping methods, are compared and the model with highest accuracy used in a self-programmed interface for automatically processing textile images in the future. Finally, the digital archive system has been validated using six garments of silk fabric relics. The color differences after the color image reproduction are all less than 3.00ΔE*ab, indicating acceptable color reproduction of the system. The images after color reproduction have also been evaluated subjectively by experts from the museum and the results are considered satisfactory. Our results show that the newly designed “Qianlong Palette” color chart exhibits superior performance over the conventional color chart in effectively predicting the color of the silk fabrics. The self-programmed graphical user interface for image color management can serve as a powerful tool to truly reproduce the color of various silk fabric relics in museums in the future and digitally archive those valuable cultural relics for different uses.

Analysis Model of Image Colour Data Elements Based on Deep Neural Network.

At present, the classification method used in image colour element analysis in China is still based on subjective visual evaluation. Because the evaluation process will inevitably be disturbed by human factors, it will not only have low efficiency but also produce large errors. To solve the above problems, this paper proposes an image colour data element analysis model based on depth neural network. Firstly, intelligent analysis of image colour data elements based on tensorflow is constructed, and the isomerized tensorflow framework is designed with the idea of Docker cluster to improve the efficiency of image element analysis. Secondly, considering the time error and spatial error diffusion model in the process of image analysis, the quantization modified error diffusion model is replaced by the original model for more accurate colour management. Image colour management is an important link in the process of image reproduction; the rotating principal component analysis method is used to correct and analyze the image colour error. Finally, using the properties of transfer learning and convolution neural network, an image colour element analysis model based on depth neural network is established. Large-scale image data is collected, and the effectiveness and reliability of the algorithm are verified from different angles. The results show that the new image colour analysis method can not only reveal the true colour components of the target image; furthermore, the real colour component of the target image also has high spectral data reconstruction accuracy, and the analysis results have strong adaptability.

Color alteration with ceramic veneers according to the tooth type and preparation step: A clinical analysis

Statement of problemAchieving accurate tooth color is important in esthetic dental treatments; however, clinical studies evaluating how ceramic veneer procedures affect color alterations are lacking. PurposeThe purpose of this clinical study was to assess the color alteration during treatment with ceramic veneers and to correlate these changes with the tooth treated and veneer thickness. Material and methodsTen patients who underwent esthetic treatment were enrolled as participants. Color analysis with the VITA spectrophotometer was performed at baseline, after tooth preparation, immediately after cementation (Final 1), and after 6 to 12 months of follow-up (Final 2). Data of L∗, a∗, b∗, shade guide unit (SGU), ΔEab, ΔE00, and variation in SGU (ΔSGU) were obtained. Thickness of the veneer was also recorded. Each participant was considered as a statistical block, and the mean results for each tooth (maxillary central incisors, lateral incisors, and canines) were presented. Data were evaluated by the mixed model for repeated measures and Tukey-Kramer post hoc test (L∗, a∗, b∗), generalized linear models (thickness, ΔEab, ΔE00, SGU), Friedman test (ΔSGU), and the Pearson test was used to correlate veneer thickness and color change (ΔEab, ΔE00, ΔSGU) (α=.05). ResultsAmong the teeth treated, no differences were found in ceramic thicknesses. The mean thickness was 1.03 mm for central incisors, 0.96 for lateral incisors, and 0.89 for canines. The b∗ values increased significantly after preparation and decreased after cementation (P<.001). For ΔEab, ΔE00, and ΔSGU, there was no statistically significant difference between the types of tooth at each respective time of analysis (P>.05). Regardless of the tooth, a decrease in the SGU score was detected after cementation (P=.015). After tooth preparation, there was significant correlation (P<.05) between ceramic thickness and ΔSGU (r=0.36). ConclusionsTreatment with ceramic veneers resulted in color change, mainly with respect to the b∗ axis (yellow), producing objective differences in the ΔE analysis. Although ceramic thickness correlated with the VITA scale change (ΔSGU) after tooth wear or preparation, ceramic thickness did not differ among tooth types and did not correlate with changes in color parameters after cementation. (P>.05).