Color Image Research Articles

Multiple Color Image Encryption System Based on Hybrid Digital/Optical Techniques and Assisted by 18D Memristive Chaos

Multiple Color Image Encryption System Based on Hybrid Digital/Optical Techniques and Assisted by 18D Memristive Chaos

Full van der Waals Ambipolar Ferroelectric Configurable Optical Hetero-Synapses for In-Sensor Computing.

The rapid development of visual neuromorphic hardware can be attributed to their ability to capture, store and process optical signals from the environment. The main limitation of existing visual neuromorphic hardware is that the realization of complex functions is premised on the increase of manufacturing cost, hardware volume and energy consumption. In this study, we demonstrated an optical synaptic device based on a three-terminal van der Waals (vdW) heterojunction that can realize the sensing functions of light wavelength and intensity as well as short-term and long-term synaptic plasticity. In the image recognition task, we constructed an optical reservoir neural network (ORNN) and a visible light communication system (VLC) composed of this optical synaptic device. The ORNN has a recognition rate of up to 84.9% for 50 000 color images in 10 categories in the CIFAR-10 color image dataset, and the VLC system can achieve high-speed transmission with an ultra-low power consumption of only 0.4 nW. This work shows that through reasonable design, vdW heterojunction structures have great application potential in low-power multifunctional fusion application tasks such as visual bionics.

DDNet: Depth Dominant Network for Semantic Segmentation of RGB-D Images

Convolutional neural networks (CNNs) have been widely applied to parse indoor scenes and segment objects represented by color images. Nonetheless, the lack of geometric and context information is a problem for most RGB-based methods, with which depth features are only used as an auxiliary module in RGB-D semantic segmentation. In this study, a novel depth dominant network (DDNet) is proposed to fully utilize the rich context information in the depth map. The critical insight is that obvious geometric information from the depth image is more conducive to segmentation than RGB data. Compared with other methods, DDNet is a depth-based network with two branches of CNNs to extract color and depth features. As the core of the encoder network, the depth branch is given a larger fusion weight to extract geometric information, while semantic information and complementary geometric information are provided by the color branch for the depth feature maps. The effectiveness of our proposed depth-based architecture has been demonstrated by comprehensive experimental evaluations and ablation studies on challenging RGB-D semantic segmentation benchmarks, including NYUv2 and a subset of ScanNetv2.

Global Exponential Synchronization of Delayed Quaternion-Valued Neural Networks via Decomposition and Non-Decomposition Methods and Its Application to Image Encryption

With the rapid advancement of information technology, digital images such as medical images, grayscale images, and color images are widely used, stored, and transmitted. Therefore, protecting this type of information is a critical challenge. Meanwhile, quaternions enable image encryption algorithm (IEA) to be more secure by providing a higher-dimensional mathematical system. Therefore, considering the importance of IEA and quaternions, this paper explores the global exponential synchronization (GES) problem for a class of quaternion-valued neural networks (QVNNs) with discrete time-varying delays. By using Hamilton’s multiplication rules, we first decompose the original QVNNs into equivalent four real-valued neural networks (RVNNs), which avoids non-commutativity difficulties of quaternions. This decomposition method allows the original QVNNs to be studied using their equivalent RVNNs. Then, by utilizing Lyapunov functions and the matrix measure method (MMM), some new sufficient conditions for GES of QVNNs under designed control are derived. In addition, the original QVNNs are examined using the non-decomposition method, and corresponding GES criteria are derived. Furthermore, this paper presents novel results and new insights into GES of QVNNs. Finally, two numerical verifications with simulation results are given to verify the feasibility of the obtained criteria. Based on the considered master–slave QVNNs, a new IEA for color images Mandrill (256 × 256), Lion (512 × 512), Peppers (1024 × 1024) is proposed. In addition, the effectiveness of the proposed IEA is verified by various experimental analysis. The experiment results show that the algorithm has good correlation coefficients (CCs), information entropy (IE) with an average of 7.9988, number of pixels change rate (NPCR) with average of 99.6080%, and unified averaged changed intensity (UACI) with average of 33.4589%; this indicates the efficacy of the proposed IEAs.

Color perception and its relation to dental anxiety in children.

One of the major causes of dental anxiety in children is their first impression of the dental environment. Even minor details, such as the choice of color in a dental setting and the color of dental equipment, can positively influence a child's behavior. The aim of the study was to assess the relationship between the emotions in children and color combinations in a pediatric setting. The study involved 200 children (99 boys and 101 girls) aged between 6 and 12 years who visited the dental clinics at the College of Dentistry, Jazan University, for the first time between November 2017 and January 2018. The participants were divided into 2 groups based on age. The younger children group included participants aged from 6 to 9 years, while the older children group included participants aged from 10 to 12 years. Anxiety levels were recorded using the Modified Dental Anxiety Scale. Colored pencils and images of emoticons were provided to all children, who were instructed to color the negative and positive emoticons with their preferred colors. The analysis of anxiety levels among children in both groups revealed statistically significant differences across sexes in the younger age group, with girls being more anxious than boys (p = 0.003). Additionally, a statistically significant difference was observed in the choice of colors by children of both sexes in 2 age groups (p = 0.001). Most children were inclined towards bright colors and used them to express their emotions. The incorporation of colors in a dental setting could invoke positive emotions in children. Hence, the use of colors in the workplace has the potential to ease anxiety.

Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning-Based Computer-Aided Diagnosis Schemes in Breast Mass Classification.

The purpose of this study is to investigate the impact of using morphological information in classifying suspicious breast lesions. The widespread use of deep transfer learning can significantly improve the performance of themammogram based CADx schemes.However, digital mammograms are grayscale images, while deep learning models are typically optimized using the natural images containing three channels. Thus, it is needed to convert the grayscale mammograms into three channel images for the input of deep transfer models. This study aims to developa novel pseudo color image generation methodwhich utilizesthe mass contour information to enhance the classification performance.Accordingly, a total of 830 breast cancer cases were retrospectively collected, which contains 310 benign and 520 malignant cases, respectively. For each case, a total of four regions of interest (ROI) are collected from the grayscale images captured for both the CC and MLO views of the two breasts. Meanwhile, a total of seven pseudo color image sets are generated as the input of the deep learning models, which arecreated through a combination of the original grayscale image, a histogram equalized image, a bilaterally filtered image, and a segmented mass. Accordingly, the output features from four identical pre-trained deep learning models are concatenated and then processed by a support vector machine-based classifier to generate the final benign/malignant labels. The performance of each image set was evaluated and compared. The results demonstrate that the pseudo color sets containing the manually segmented mass performed significantly better than all other pseudo color sets, which achieved an AUC (area under the ROC curve) up to 0.889 ± 0.012 and an overall accuracy up to 0.816 ± 0.020, respectively. At the same time, the performance improvement is also dependent on the accuracy of the mass segmentation. The results of this study support our hypothesis that adding accurately segmented mass contours can provide complementary information, thereby enhancing the performance of the deep transfer model in classifying suspicious breast lesions.

Metal-to-insulator phase transition of molybdenum oxide for reversible writing and erasing information encryption

Abstract We have reported a theoretical device design for reversible writing and erasing information encryption using a simple two layered structure, which consists of a MoOx layer and a SiO2 space layer on a silver substrate. The MoOx layer can be switched between two phases, i.e., metallic MoO2 and insulating MoO3. The phase transition of MoOx possesses very complicated processes, which makes it advantageous for information decryption. Generally, in the proposed structure with metallic MoO2, various colors can be generated due to the F-P enhanced absorption at wavelengths where construction interference occurs. However, owing to the low loss of insulating MoO3 in visible region, the absorption is low though out the whole visible region, thus all colors can be erased to white. Therefore, the color image information can be written and erased with our proposed structure by switching MoOx between phases of metallic MoO2 and insulating MoO3. The proposed image encryption device can be acquired easily by masking and coating processes. We believe our results provides a new way in designing encryption devices.

Enhancing color image watermarking via fast quaternion Schur decomposition: a high-quality blind approach

Enhancing color image watermarking via fast quaternion Schur decomposition: a high-quality blind approach

The impact of linked color imaging on adenoma detection rate in colonoscopy: a systematic review and meta-analysis.

Colorectal cancer prevention relies on surveillance colonoscopy, with the adenoma detection rate as a key factor in examination quality. Linked color imaging (LCI) enhances lesion contrast and improves the examination performance. This systematic review and meta-analysis aimed to evaluate the effect of LCI on adenoma detection rate in adults who underwent colonoscopy. We searched the Medline, PubMed, BIREME, LILACS, and Scientific Electronic Library Online databases for randomized controlled trials comparing the use of LCI versus white light (WL), published up to March 2023. The outcomes included lesion characteristics, number of adenomas per patient, and the additional polyp detection rate. Sixteen studies were included in the analysis, which showed that LCI was more accurate than WL in detecting adenomas, with an increased number of adenomas detected per patient. Although LCI performed well in terms of lesion size, morphology, and location, the subgroup analyses did not reveal any statistically significant differences between LCI and WL. The addition of LCI did not result in significant improvements in the detection of serrated lesions, and there were no differences in the withdrawal time between groups. LCI has been shown to be effective in detecting colonic lesions, improving the number of adenomas detected per patient and improving polyp detection rate without negatively affecting other quality criteria in colonoscopy.

An image encryption algorithm for colour images based on a cellular neural network and the Chua's chaotic system

In the internet era, image encryption technology plays a crucial role in ensuring the security of image information. Aiming at the problem that low-dimensional chaotic encryption has a relatively small key space and is vulnerable to chosen-plain attacks, this paper proposed a novel colour image block encryption algorithm based on the cellular neural network and the Chua's chaotic system. Firstly, the plain is separated into three primary colours. And Fourier transform is applied to achieve optical transformation, generating six chaotic sequences iteratively. Secondly, the chaotic sequences were combined in pairs. And then combined with the plain to form three sequences, which were used in the diffusion process. Finally, the improved Chua's chaotic system is used to scramble the image. Both diffusion and scrambling are performed synchronously during encryption. Through the above operation, we get the final colour encrypted picture. The experimental results show that the algorithm has excellent encryption effect and can resist common attacks.

Quantum Color Image Encryption: Dual Scrambling Scheme based on DNA Codec and Quantum Arnold Transform

Abstract In the field of Internet, image is of great significance to information transmission. Meanwhile, how to ensure and improve its security has become the focus of research in the world today. We combine DNA codec with Quantum Arnold Transform (QArT) to propose a new double encryption algorithm for quantum color images. to improve the security and robustness of image encryption.Firstly, we utilize the biological characteristics of DNA codecs to perform encoding and decoding operations on pixel color information in quantum color images, and achieve pixel-level diffusion. Secondly, we use QArT to scramble the position information of quantum images and use the operated image as the key matrix for quantum XOR operations. All quantum operations in this paper are reversible, so the decryption operation of the ciphertext image can be realized by the reverse operation of the encryption process. We conduct simulation experiments on encryption and decryption using three color images of ”Monkey”, ”Flower” and ”House”. The experimental results show that the peak value and correlation of the encrypted images on the histogram have good similarity, and the average NPCR of RGB three-channel is 99.61%, the average UACI is 33.41%, and the average information entropy is about 7.9992. In addition, the robustness of the proposed algorithm is verified by the simulation of noise interference in the actual scenario.

The female and emended male description of the USA Interior Highlands endemic Allocapnia oribata Poulton & Stewart, 987 (Plecoptera: Capniidae)

The female of Allocapnia oribata Poulton & Stewart, 1987 is newly associated by tree- and genetic distance-based phylogenetic methods using the mitochondrial cytochrome c oxidase subunit I gene barcode fragment sequence data. The first description of the female and an emended description of the male of A. oribata are presented. Supporting data includes color images, scanning electron micrographs, and genetic analysis of DNA barcodes. Corrections to the holotype and paratype localities are presented with the help of B.C. Poulton. Additional notes on the phylogeny and endemism of Allocapnia Claassen, 1928 are provided.

Point cloud segmentation method based on an image mask and its application verification

Abstract Accurately perceiving three-dimensional (3D) environments or objects is crucial for the advancement of artificial intelligence (AI) interaction technologies. Currently, various types of sensors are employed to obtain point cloud data for 3D object detection or segmentation tasks. While this multi-sensor approach provides more precise 3D data than monocular or stereo cameras, it is also more expensive. The advent of RGB-D cameras, which provide both RGB images and depth information, addresses this issue.
In this study, we propose a point cloud segmentation method based on image masks. By using an RGB-D camera to capture color and depth images, we generate image masks through object recognition and segmentation. Given the mapping relationship between RGB image pixels and point clouds, these image masks can be further used to extract the point cloud data of the target objects. The experimental results revealed that the average accuracy of target segmentation was 84.78%, which was close to that of PointNet++. Compared with three traditional segmentation algorithms, the accuracy was improved by nearly 23.97%. The running time of our algorithm is reduced by 95.76% compared to the PointNet++ algorithm, which has the longest running time; and by 15.65% compared to the LCCP algorithm, which has the shortest running time among traditional methods. Compared with PointNet++, the segmentation accuracy was improved. This method addressed the issues of low robustness and excessive reliance on manual feature extraction in traditional point cloud segmentation methods, providing valuable support and reference for the accurate segmentation of 3D point clouds.

Virtual cleaning of sooty mural hyperspectral images using the LIME model and improved dark channel prior

Murals, as important carriers of cultural heritage and historical records, showcase artistic, aesthetic, social, and political significance. In ancient times, religious activities such as burning incense and candles in temples led to many murals being polluted by soot, causing them to darken, lose details, and, in severe cases, completely blacken. As a result, the development of efficient virtual cleaning methods has become a key strategy for addressing this issue. In this study, we use synthetic true colour and false colour images in different bands of the hyperspectral spectrum, and use a guided filter fusion technique to fuse these two images into a new image of the sooty mural. Through analyzing the histograms and colour distribution scatterplots of the synthetic sooty mural images, we observed significant similarities to low-luminance images. To enhance the synthesized murals, we applied the LIME model. In addition, comparisons of the histograms and colour distribution scatterplots of the enhanced sooty mural images with those of haze images revealed notable similarities. Therefore, we applied the dark channel prior algorithm to remove soot from the mural images. Considering that soot particles are larger than haze particles, we introduced guided filtering to refine the transmission map and created a nonlinear transformation function to enhance its details. In terms of both visual perception and quantitative analysis, the proposed method significantly outperforms previous methods in the virtual cleaning of sooty murals. This technology can not only restore the colours and details of murals but also provide new clues for subsequent mural studies, allowing people to once again appreciate the true beauty of the murals.

Effective low-exposure color image enhancement based on histogram equalization with spatial contextual information

Abstract Color image enhancement refers to improving the visual quality of color images. This enhancement aims to make the images more visually appealing, precise, and easier to interpret, often by accentuating essential features or details while minimizing noise or distortion. Effective image enhancement is crucial in numerous fields like medical imaging; enhancing the medical image is vital for accurate diagnosis , and remote sensing . In security and surveillance, enhanced clarity in footage from surveillance cameras, especially in low-light scenarios, is crucial for identifying subjects and activities. However, image enhancement faces several challenges, like noise amplification and Over-enhancement, leading to unnatural-looking images with exaggerated or distorted features. In this paper, low-exposed or night color images are considered for enhancement, and this paper introduces exposure-based recursive histogram equalization techniques along with an Energy Curve instead of the conventional histogram, the energy curve is similar to a histogram based on spatial Contextual Information of an image. The proposed methods are Recursive Exposure-based Sub-image Histogram Equalization, Recursively Separated Exposure-based Sub-image Histogram Equalization, and Exposure-based Sub-image Histogram Equalization techniques considering spatial contextual information of images using an Energy Curve to improve results. These methods were tested on several publicly available datasets with low-contrast color images and compared with HE, BBHE, DSHE, CLAHE, ESIHE, R-ESIHE, and RS-ESIHE. The proposed techniques are evaluated using parameters like AMBE, PSNR, MSE, Entropy, SSIM, and FSIM. The average PSNR values for eight images using the aforementioned techniques were 9.10817, 20.80568, 12.82645, 10.39347, 16.27458, 15.19979, and 14.59595, respectively. In contrast, the proposed ESIHE with Energy Curve achieved a PSNR of 22.31585, outperforming other methods across multiple metrics, particularly excelling in noise reduction (PSNR), error minimization (MSE: 193.44), structural similarity (SSIM: 0.733319), and feature retention (FSIM: 0.917678). The comparison demonstrates that enhancement methods can be significantly improved by considering spatial contextual information.

Fast Color Image Encryption Algorithm Based on DNA Coding and Multi-Chaotic Systems

At present, there is a growing emphasis on safeguarding image data, yet conventional encryption methods are full of numerous limitations. In order to tackle the limitations of conventional color image encryption methodologies, such as inefficiency and insufficient security, this paper designs an expedited encryption method for color images that uses DNA coding in conjunction with multiple chaotic systems. The encryption algorithm proposed in this paper is based on three-dimensional permutation, global scrambling, one-dimensional diffusion and DNA coding. First of all, the encryption algorithm uses three-dimensional permutation algorithms to scramble the image, which disrupts the high correlation among the image pixels. Second, the RSA algorithm and the SHA-256 hashing algorithm are utilized to derive the starting value necessary for the chaotic system to produce the key. Third, the image is encrypted by using global scrambling and one-dimensional diffusion. Finally, DNA coding rules are used to perform DNA computing. The experimental results indicate that the encryption scheme exhibits a relatively weak inter-pixel correlation, uniform histogram distribution, and an information entropy value approaching eight. This shows that the proposed algorithm is able to protect the image safely and efficiently.

Ethnic Peculiarities and Inter-ethnic Parallels in the Traditional Material Culture of the Hungarians of Ugocsa

The Hungarians living in the present-day Transcarpathian region have lived peacefully for centuries together with the majority Ruthenian/Ukrainian population, as well as with the Romanian, German, Roma and other ethnic groups, who live in a similar minority to the Hungarians. Ethnographers and tourists visiting the region are convinced that these nationalities have retained the characteristics of their culture to this day. At the same time, it is worth observing how this long historical coexistence is reflected in the way of life and mentality of these people. The parallels between Ruthenian and Hungarian language and folklore, folk customs are excellent examples of interethnic relations, but I have also encountered similar examples when researching the traditional material culture of the villages in Ugocsa. In the field of folk architecture, for example, the gate called tőkés kapu, as well as the abora, aszaló [the dryer] and the barn. Interethnic phenomena between Hungarians, Ruthenians and other nationalities of the region can also be observed in folk costumes (the guba, or the shoes called bochkor). Throughout history, in Transcarpathia, which belonged to different state formations, it was noticeable that culture was not strictly tied to peoples. Thanks to the tolerance shown towards each other, the nationalities of the region have preserved their ethnic and religious characteristics and identity, but we can also find many similarities in their cultures. When studying the interactions that mutually enrich each other's culture and provide a colorful and attractive image to the region, it is often impossible to find out who borrowed from whom and when. During the ethnographic research of the local communities, the main point is to take into account the ethnic interaction of the nationalities living here, as the folk culture of the local Hungarians is determined by the aggregation of the cultures of different ethnicities.

An adaptive robust watermarking scheme based on chaotic mapping

Digital images have become an important way of transmitting information, and the risk of attacks during transmission is increasing. Image watermarking is an important technical means of protecting image information security and plays an important role in the field of information security. In the field of image watermarking technology, achieving a balance between imperceptibility, robustness, and embedding capacity is a key issue. To address this issue, this paper proposes a high-capacity color image adaptive watermarking scheme based on discrete wavelet transform (DWT), Heisenberg decomposition (HD), and singular value decomposition (SVD). In order to enhance the security of the watermark, Logistic chaotic mapping was used to encrypt the watermark image. By adaptively calculating the embedding factor through the entropy of the cover image, and then combining it with Alpha blending technology, the watermark image is embedded into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. In addition, the robustness of the algorithm was further improved through singular value correction methods. The experimental results show that the average PSNR and SSIM of the watermarking scheme are 45.3437dB and 0.9987, respectively. When facing various attacks, the average NCC of the extracted watermark reaches above 0.95, indicating good robustness. The embedding capacity of this scheme is 0.6667bpp, which is higher than other watermarking schemes, and the average running time is 1.1136 seconds, which is better than most schemes.

A Multilayer Nonlinear Permutation Framework and Its Demonstration in Lightweight Image Encryption

As information systems become more widespread, data security becomes increasingly important. While traditional encryption methods provide effective protection against unauthorized access, they often struggle with multimedia data like images and videos. This necessitates specialized image encryption approaches. With the rise of mobile and Internet of Things (IoT) devices, lightweight image encryption algorithms are crucial for resource-constrained environments. These algorithms have applications in various domains, including medical imaging and surveillance systems. However, the biggest challenge of lightweight algorithms is balancing strong security with limited hardware resources. This work introduces a novel nonlinear matrix permutation approach applicable to both confusion and diffusion phases in lightweight image encryption. The proposed method utilizes three different chaotic maps in harmony, namely a 2D Zaslavsky map, 1D Chebyshev map, and 1D logistic map, to generate number sequences for permutation and diffusion. Evaluation using various metrics confirms the method’s efficiency and its potential as a robust encryption framework. The proposed scheme was tested with 14 color images in the SIPI dataset. This approach achieves high performance by processing each image in just one iteration. The developed scheme offers a significant advantage over its alternatives, with an average NPCR of 99.6122, UACI of 33.4690, and information entropy of 7.9993 for 14 test images, with an average correlation value as low as 0.0006 and a vast key space of 2800. The evaluation results demonstrated that the proposed approach is a viable and effective alternative for lightweight image encryption.

PSO-based Quaternion Fourier Transform steganography: Enhancing imperceptibility and robustness through multi-dimensional frequency embedding

This paper presents a novel steganography technique using the Quaternion Fourier Transform (QFT) in the 4D frequency domain to enhance imperceptibility and robustness in digital image embedding. Steganography, the art of hiding information within media, faces challenges in balancing security, imperceptibility, and robustness. To address this, we leverage the multi-dimensional properties of quaternions, enabling the embedding of secret data in both grayscale and color images. For grayscale images, two quaternion dimensions are utilized for intensity and secret data, while for color images, all four dimensions are employed with one reserved for metadata. The research question centers on how to maximize spatial dispersion and color similarity while maintaining high imperceptibility and robustness against attacks. Experimental results show that the proposed method improves visual imperceptibility by more than 4 % and exhibits a 13 % increase in robustness against common steganalysis attacks compared to the best state-of-the-art existing technique. These advancements highlight the potential of this method for applications in secure communication, digital watermarking, and copyright protection. By combining the quaternion mathematical framework with a novel optimization strategy, this approach significantly improves upon traditional steganography methods.