Color Image Research Articles

Blue Wavelength of Scanning Laser Ophthalmoscope Potentially Detects Arteriosclerotic Lesions in Diabetic Retinopathy.

(1) Background: The fundus examination is one of the best and popular methods in the assessment of vascular status in the human body. Direct viewing of retinal vessels by ophthalmoscopy has been utilized in judging hypertensive change or arteriosclerosis. Recently, fundus imaging with the non-mydriatic scanning laser ophthalmoscope (SLO) has been widely used in ophthalmological clinics since it has multimodal functions for optical coherence tomography or angiography with contrast agent dye. The purpose of this study was to examine the utility in detecting arteriosclerosis of retinal vessels in SLO images; (2) Methods: Both color and blue standard field SLO images of eyes with diabetic retinopathy (DR) were examined retrospectively. Retinal arteriosclerosis in color SLO images was graded according to the Scheie classification. Additionally, characteristics of retinal arterioles in blue SLO images were identified and examined for their relevance to arteriosclerosis grades, stages of DR or general complications; (3) Results: Relative to color fundus images, blue SLO images showed distinct hyper-reflective retinal arterioles against a monotone background. Irregularities of retinal arterioles identified in blue SLO images were frequently observed in the eyes of patients with severe arteriosclerosis (Grade 3: 79.0% and Grade 4: 81.8%). Furthermore, the findings on arterioles were more frequently associated with the eyes of DR patients with renal dysfunction (p < 0.05); (4) Conclusions: While color SLO images are equally as useful in assessing retinal arteriosclerosis as photography or ophthalmoscopy, the corresponding blue SLO images show arteriosclerotic lesions with high contrast in a monotone background. Retinal arteriosclerosis in eyes of advanced grades or advanced DR frequently show irregularities of retinal arterioles in the blue images. The findings of low, uneven, or discontinuous attenuation were easier to find in blue than in color SLO images. Consequently, blue SLO images can show pathological micro-sclerosis in retinal arterioles and are potentially one of the safe and practical methods for the vascular assessment of diabetic patients.

Efficient enhancement of low-rank tensor completion via thin QR decomposition.

Low-rank tensor completion (LRTC), which aims to complete missing entries from tensors with partially observed terms by utilizing the low-rank structure of tensors, has been widely used in various real-world issues. The core tensor nuclear norm minimization (CTNM) method based on Tucker decomposition is one of common LRTC methods. However, the CTNM methods based on Tucker decomposition often have a large computing cost due to the fact that the general factor matrix solving technique involves multiple singular value decompositions (SVDs) in each loop. To address this problem, this article enhances the method and proposes an effective CTNM method based on thin QR decomposition (CTNM-QR) with lower computing complexity. The proposed method extends the CTNM by introducing tensor versions of the auxiliary variables instead of matrices, while using the thin QR decomposition to solve the factor matrix rather than the SVD, which can save the computational complexity and improve the tensor completion accuracy. In addition, the CTNM-QR method's convergence and complexity are analyzed further. Numerous experiments in synthetic data, real color images, and brain MRI data at different missing rates demonstrate that the proposed method not only outperforms in terms of completion accuracy and visualization, but also conducts more efficiently than most state-of-the-art LRTC methods.

Color Image Hybrid Noise Filtering Algorithm based on Deep Convolution Neural Network

To solve the problems of the classical color image hybrid noise filtering method, a deep convolutional neural network improved by evolutionary strategy and jump connection is proposed and applied to the filtering noise reduction of color images. First, the color information of the image is described quantitatively by digital means. The common method is to build color space model. According to the characteristics of color and the needs of human vision, mathematical algorithms are used to convert images into machine recognizable data. The distance between pixels is measured according to the difference of pixels in the color image determined above. Then, the probability density function and noise probability density function of Gaussian noise are calculated to determine the hybrid noise feature points of color image. The filtering algorithm structure designed this time is as follows: A color image hybrid noise filter is used to map the noise points in the mapped image to the feature space, and linear regression is performed on the noise point data. Relaxation variables are introduced in the network to improve the denoising ability. The experimental results show that the Peak Signal to Noise Ratio and structural similarity index values of the filtering algorithm designed in this study are higher than the two methods in the literature. The color image hybrid noise filtering model designed in this study has good filtering performance, good image cleanliness, and high filtering efficiency.

Using Support Vector Machine to Detect Data Hiding in Color Images

Background: A steganography investigation model that uses improved dim scale List of capabilities, for identifying information inside uncompressed RGB pictures. Materials and Methods: in this paper, a bunch of 2000 RGB pictures was made utilizing normal tones, in BMP design with a size of 512 x 512 pixels. Clean pictures are Inserted secret picture information, utilizing two Payload plans, 2 pieces for every channel (BPC) and 4 pieces for each channel. The arrangement of chosen highlights comprises of 24 elements for every variety channel, and 72 highlights for each variety channel The picture, incorporates dim-level co-event grid highlights, entropy Elements, and factual proportions of variety. The list of capabilities components is determined as Individual channels, joined into picture vector highlights. The cycle depends on steganography examination On AI, utilizing Paired vector machine (SVM) support Execute the exercise manual in MATLAB. Results: The outcomes showed that the new method had overcome the benchmark with more than 80% identification accuracy Conclusion : a new Steganalysis model was introduced by using SVM. The model can recognize the presence of stowed-away information inside RGB variety pictures, removing the highlights from datasets of perfect and messy pictures and ordering utilizing the support Vector Machine calculation. the results were compared with the DNA algorithm.

Rapid and non-destructive identification of Panax ginseng origins using hyperspectral imaging, visible light imaging, and X-ray imaging combined with multi-source data fusion strategies

The geographical origin of Panax ginseng significantly influences its nutritional value and chemical composition, which in turn affects its market price. Traditional methods for analyzing these differences are often time-consuming and require substantial quantities of reagents, rendering them inefficient. Therefore, hyperspectral imaging (HSI) in conjunction with X-ray technology were used for the swift and non-destructive traceability of Panax ginseng origin. Initially, outlier samples were effectively rejected by employing a combined isolated forest algorithm and density peak clustering (DPC) algorithm. Subsequently, random forest (RF) and support vector machine (SVM) classification models were constructed using hyperspectral spectral data. These models were further optimized through the application of 72 preprocessing methods and their combinations. Additionally, to enhance the model’s performance, four variable screening algorithms were employed: SelectKBest, genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), and permutation feature importance (PFI). The optimized model, utilizing second derivative, auto scaling, permutation feature importance, and support vector machine (2nd Der-AS-PFI-SVM), achieved a prediction accuracy of 93.4 %, a Kappa value of 0.876, a Brier score of 0.030, an F1 score of 0.932, and an AUC of 0.994 on an independent prediction set. Moreover, the image data (including color information and texture information) extracted from color and X-ray images were used to construct classification models and evaluate their performance. Among them, the SVM model constructed using texture information from X -ray images performed the best, and it achieved a prediction accuracy of 63.0 % on the validation set, with a Brier score of 0.181, an F1 score of 0.518, and an AUC of 0.553. By implementing mid-level fusion and high-level data fusion based on the Stacking strategy, it was found that the model employing a high-level fusion of hyperspectral spectral information and X-ray images texture information significantly outperformed the model using only hyperspectral spectral information. This advanced model attained a prediction accuracy of 95.2 %, a Kappa value of 0.912, a Brier score of 0.027, an F1 score of 0.952, and an AUC of 0.997 on the independent prediction set. In summary, this study not only provides a novel technical path for fast and nondestructive traceability of Panax ginseng origin, but also demonstrates the great potential of the combined application of HSI and X-ray technology in the field of traceability of both medicinal and food products.

LPI radar waveform recognition model based on multiple feature image and quasi-residual attention module

Abstract Detecting and classifying the modulation type of the intercepted noisy LPI radar waveform has become a hot topic in the field of Electronic Countermeasures (ECM). In this paper, we propose a recognition model based on multiple feature images (MFI) and convolutional neural networks (CNN) with the quasi-residual attention module (QRAM). The core technologies of this model are divided into two parts. One is the MFI that combines the ambiguity function image (AFI) of the complete signal, covariance matrix image (CMI) of the reconstructed signal, and short-time Fourier transform image (STFTI) of the truncated signal into RGB color image in signal processing as the input for recognition network. The other is CNN with a QRAM recognition network, in which the input is sliced into two mappings called identity mapping and residual mapping to build the quasi-residual module, and the attention module is embedded in each mapping to denoise and enhance the feature. The performance of the MFI-QRAM model is demonstrated by recognizing 12 modulation types of the signals defined in this paper. The simulation experiments show that the model has strong robustness with the number of train sets and the variation of SNR. The overall probability of successful recognition (PSR) is 96.17% when the SNR is −6 dB.

First record of Canthesancus gulo Stal, 1863 (Reduviidae, Stenopodainae) from Maharashtra, India

The brownish castaneous spot assassin bug Canthesancus gulo Stal, 1863 is reported from Maharashtra for the first time, as the first species of this genus in the Maharashtra state. The diagnostic characters, colour images, and current geographical distribution of species are given.

Using DNA Algorithm to Hide a Compressed Ciphertext in Colored Image

Cryptography is the fundamental key to keeping data that are more trusted for the increasing reliability in the digital community. Steganography is widely used to protect any hidden data in the media so that it may not get hacked. The conventional protection styles do not provide the requirements of cyber security under the new hacker's techniques that are applied for any hacking data and confidential information. Moreover, using the encryption algorithms without providing extra barriers alone could provide an opportunity to be breached therefore, boosting the levels of protection is really urgent to shield information against any intruders. This paper submits a proposed algorithm that can be applied to overcome the risks of hacking and increase the data protection. First, this algorithm uses the DNA to encrypt the data, which is then made complex by a multiplier and compressed by using the GZIP algorithm. The final stage is to hide the compressed data in the media (color image pixels) by using the LSB method. When applying these levels of protection, the amount of data compression becomes at 75%, and the image measures proves that good results are fulfilled, for instance, Lina sample measure is for SSIM = 1, PSNR = 67.6119, MSE = 0.0115, UACI = 7.00284 and NPCR = 0.01152. This algorithm contributes to increase the level of data protection in the communication between any parties to transfer certain confidential information.

First record of Canthesancus gulo Stal, 1863 (Reduviidae, Stenopodainae) from Maharashtra, India

The brownish castaneous spot assassin bug Canthesancus gulo Stal, 1863 is reported from Maharashtra for the first time, as the first species of this genus in the Maharashtra state. The diagnostic characters, colour images, and current geographical distribution of species are given.

Clustering-based interactive image segmentation

Objectives. The task of color image segmentation without the use of preliminary training is considered. It arises, for example, when it is necessary to perform image segmentation with semantic and color properties unknown in advance immediately after their acquisition, or when the set of images intended for segmentation is too small, as well as when performing preliminary "exploratory" analysis of images. In such cases, powerful neural network and other segmentation tools that require deep learning can not be used.Methods. An algorithm for interactive image segmentation is proposed, based on the analysis of the colors of areas selected interactively. First, in interactive mode, the image areas belonging to the objects are selected very approximately, and then regions belonging to the background are chosen. In the next step, the set of colors of the selected object areas and the set of colors of the selected background areas are clustered separately by one of the clustering algorithms, for example, k-means, fuzzy c-means, or the multi-level clustering algorithm proposed by the author. After this, non-informative elements are removed from the set of cluster centers describing the objects and the set of clusters presenting the background. The modified sets of object and background cluster centers are used for image segmentation.Results . The constructed algorithm allows selection of the required objects in color images if the colors of the objects and the background are different. Interactive selection of object areas and background areas does not require accuracy or much effort and usually takes several tens of seconds. For selection, rectangular areas that lie entirely inside the object images, and rectangular areas that belong completely to the background can be used. Below an example of interactive regions selection and color image segmentation is shown.Conclusion. The experiments performed showed the effectiveness of the proposed approach to segmenting color images. It can be used in cases where the semantic and color properties of images are not known in advance, and in cases where the use of more powerful deep learning methods, including neural networks, is too expensive or impossible.

ОСНОВАНИЯ И КОМПАРАТИВИСТСКИЙ АНАЛИЗ РУССКОГО ЭПОСА

The investigation of Russian folklore has not yet been fully institutionalized in literature, cinema, and theater. In Russian literature, for example, there are no novels about Koshchei the Immortal, Ivan the Prince, heroes of Slavic pagan culture and many other characters of this genre. Meanwhile, these are deep, colorful images organically integrated into an equally vivid context. Many great writers, such as Thomas Mann, have written about such gaps. The goal is to identify the main typological characteristics of the Russian epic, which, in our opinion, has been studied in this sense very one-sidedly. The methodology is a comparative analysis of various approaches (mythological, historical, structural, etc.) closely interconnected with the characteristic features and structure of epics. At the same time, considerable attention is the harmonious combination of various motifs of the epic: heroes, Kalik-passers-by, epic time, pagan influences, hyperbolization, etc. An attempt is made not to accept, but to take into account various influences on such studies (pre-revolutionary, post-revolutionary, inspired by the Marxist approach, structural analysis, which began to be used in the study of Russian epic since the 1970s). The Russian epic, thanks to a huge number of historical, social, and cultural connections, naturally interacts with the epics of the republics (union and autonomous) that were part of the former USSR, which opens up wide opportunities for comparative studies of various types. Paths are outlined for a comparative analysis of the Russian epic with the epics of the East Slavic peoples, as well as with the Yakut heroic epic Olonkho. The authors' further plans involve continuing the study of Russian folklore in the genre of fairy tales and the field of culture of Slavic paganism. The first of them has been studied quite fully, but the structural and systemic approaches to research need to be further developed. The culture of Slavic paganism is a little-studied area where there are still no established approaches, and there are different points of view on many issues. Here you can apply the methodological approaches outlined in our monograph “Philosophy of Olonkho”.

Variational Model with Nonstandard Growth Condition in Image Restoration and Contrast Enhancement

AbstractWe propose a new variational model in Sobolev-Orlicz spaces with non-standard growth conditions of the objective functional and discuss its applications to the simultaneous contrast enhancement and denoising of color images. The characteristic feature of the proposed model is that we deal with a constrained non-convex minimization problem that lives in variable Sobolev-Orlicz spaces where the variable exponent is unknown a priori and it depends on a particular function that belongs to the domain of the objective functional. In contrast to the standard approach, we do not apply any spatial regularization to the image gradient. We discuss the consistency of the variational model, give the scheme for its regularization, derive the corresponding optimality system, and propose an iterative algorithm for practical implementations.

A scheme of hiding large-size image into small-size image based on FCdDNet.

The hiding capacity of the current information hiding field has reached a relatively high level, which can hide two color images into one color image. In order to explore a larger hidden capacity, an information hiding scheme based on an improved FCdDNet is proposed, which can hide large-size color images into small-size color images. An improved FCdDNet network is used as the main structure shared by the hidden network and the extraction network. These two networks promote and improve each other during the confrontation training process and are used in pairs. It can be seen that the proposed scheme achieves a larger information hiding capacity, and the hidden information is four times larger than the scale of the carrier image. At the same time, the visual effect after hiding is guaranteed, and the image extracted from the hidden image also has a high degree of restoration. The scheme can be applied to image authentication, secret image transmission, and other fields.

RAINBOW: Resilient Asymmetric Imaging Non-linear Bit-level Ordering with Hyperchaotic Operation for Color image Encryption

RAINBOW: Resilient Asymmetric Imaging Non-linear Bit-level Ordering with Hyperchaotic Operation for Color image Encryption

A Hybrid Domain Color Image Watermarking Scheme Based on Hyperchaotic Mapping

A Hybrid Domain Color Image Watermarking Scheme Based on Hyperchaotic Mapping

A Binocular Color Line-Scanning Stereo Vision System for Heavy Rail Surface Detection and Correction Method of Motion Distortion.

Thanks to the line-scanning camera, the measurement method based on line-scanning stereo vision has high optical accuracy, data transmission efficiency, and a wide field of vision. It is more suitable for continuous operation and high-speed transmission of industrial product detection sites. However, the one-dimensional imaging characteristics of the line-scanning camera cause motion distortion during image data acquisition, which directly affects the accuracy of detection. Effectively reducing the influence of motion distortion is the primary problem to ensure detection accuracy. To obtain the two-dimensional color image and three-dimensional contour data of the heavy rail surface at the same time, a binocular color line-scanning stereo vision system is designed to collect the heavy rail surface data combined with the bright field illumination of the symmetrical linear light source. Aiming at the image motion distortion caused by system installation error and collaborative acquisition frame rate mismatch, this paper uses the checkerboard target and two-step cubature Kalman filter algorithm to solve the nonlinear parameters in the motion distortion model, estimate the real motion, and correct the image information. The experiments show that the accuracy of the data contained in the image is improved by 57.3% after correction.

Color image encryption scheme for distributed architecture with SCFP chaotic map

Image protection mechanism in distributed cloud network is an essential component of information security field. In this paper, a novel one-dimensional sine-cosine fractional power chaotic map (SCFP) is proposed. Results of various dynamical system tests illustrate that SCFP exhibits superior chaotic behavior over its infinite positive real parameter range, whose complexity and unpredictability can guarantee the strength of image cryptosystem. Furthermore, a color image encryption scheme tailored for distributed architecture is devised. Firstly, a hybrid cryptographic mechanism is designed to perform diffusion and confusion encryption for image data and ECC public key encryption for intermediate keys. Secondly, the diffusion structure elevates processing units to row-column level, and the diffusion order is dictated by a pseudo-random sequence generated by SCFP. Thirdly, the confusion structure extends the unbiased and efficient Fisher-Yates algorithm into a 2D space, and adopts a design of dual plaintext-related key. Lastly, three techniques namely QOI lossless compression, DE information embedding and threshold secret sharing are integrated to resolve issues of data volume inflation, key synchronization difficulty and poor fault tolerance. Simulation experiments conducted on multiple color images demonstrate that the proposed scheme offers significant ciphertext randomness, sufficiently large key space and strong key sensitivity, which can ensure the integrity of image data and resist various typical cryptographic attacks, and outperforms existing schemes oriented to centralized architecture in terms of security and efficiency.

Evidence for transient morning water frost deposits on the Tharsis volcanoes of Mars

The present-day water cycle on Mars has implications for habitability and future human exploration. Water ice clouds and water vapour have been detected above the Tharsis volcanic province, suggesting the active exchange of water between regolith and atmosphere. Here we report observational evidence for extensive transient morning frost deposits on the calderas of the Tharsis volcanoes (Olympus, Arsia and Ascraeus Montes, and Ceraunius Tholus) using high-resolution colour images from the Colour and Stereo Surface Imaging System on board the European Space Agency’s Trace Gas Orbiter. The transient bluish deposits appear on the caldera floor and rim in the morning during the colder Martian seasons but are not present by afternoon. The presence of water frost is supported by spectral observations, as well as independent imagery from the European Space Agency’s Mars Express orbiter. Climate model simulations further suggest that early-morning surface temperatures at the high altitudes of the volcano calderas are sufficiently low to support the daily condensation of water—but not CO2—frost. Given the unlikely seasonal nature of volcanic outgassing, we suggest the observed frost is atmospheric in origin, implying the role of microclimate in local frost formation and a contribution to the broader Mars water cycle.

Hyperchaotic color image encryption scheme based on simultaneous color channel confusion-diffusion operations

Abstract Shortcomings have been identified in current color image encryption methods. Firstly, these methods encrypt each color channel separately, resulting in a time-consuming process and independent encrypted channels, which can make hacking easier. Secondly, the use of XOR operations between image pixel values and code values during encryption can be vulnerable. To address these issues, a novel algorithm is introduced that incorporates a new XOR operation and simultaneous encryption of color channels. This approach creates interdependence between the encrypted channels, reduces encryption time, and enhances security by introducing a more complex XOR operation. The proposed method employs a substitution technique that involves XOR operations between groups of pixels and codes, inspired by the principles of the fast Walsh-Hadamard transform algorithm. The encryption process involves several key phases that enhance the security and efficiency of the system. In the initial phase, line processing involves mixing lines from different channels and application of chaotic substitution permutation operations. Subsequently, a similar operation is applied to columns, and finally, the channels are divided into overlapping squared sub-blocks, with a newly XOR proposed chaos-based confusion operation simultaneously applied to the three-channel sub-blocks. These phases are designed to ensure interdependence between color channels and reduce encryption time, resulting in a more robust encryption method. With this method, the RGB cipher channels become mutually dependent, rendering decryption of one channel impossible without the others. The approach has been evaluated using appropriate metrics and found to be robust, efficient, and resistant to various attacks, outperforming recently published methods. It is suitable for modern image encryption applications, including those related to the Internet of Medical Things (IoMT).

Expression of Concern: Oil painting color image enhancement recognition method based on artificial intelligence: applications of an AI model in environmental research, E. Yao, Marvin White

Expression of Concern: Oil painting color image enhancement recognition method based on artificial intelligence: applications of an AI model in environmental research, E. Yao, Marvin White