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.

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.

Advanced color image encryption using third-order differential equations and three-dimensional logistic map

Image encryption stands out as a crucial technique employed to securely transmit images across the Internet. In this paper, we introduce a novel algorithm for encrypting color images. The algorithm is built upon the principles of differential equations, XOR operations, and chaotic maps. First, the plain image is three-dimensional pixel shuffled via a logistic map. Afterward, the differential equations are used as a mathematical tool for encrypting images. The third-order ordinary differential equations are used to encrypt the shuffled images. The color values of the plain image are considered coefficients for the independent variable X. Subsequently, an alternate matrix of the same size is generated using a three-dimensional logistic map, taking into account its color values as the exponents linked to the independent variable X. A set of third-order differential equations emerged, containing an equivalent number of elements as the color values present in the plain image. This set of differential equations is addressed in the following manner: combining XOR and integration three times with respect to the independent variable X for each set of obtained differential equations while treating the integration constant as zero. Ultimately, a set of ordinary equations involving the independent variable X is derived, where the coefficients of X represent color values for the cipher image. The results from experiments and the security analysis affirm the resilience of the proposed algorithm against established security attacks. It exhibits a substantial key space, heightened key sensitivity, and a strong encryption effect.

Color image restoration by filtering methods: a review

Digital images are corrupted with noise, and image denoising is an important step in image processing modules. In this review, the latest developments in filtering methods for color image restoration are analyzed. These algorithms are compared in terms of objective image quality measures and divided into major classes, such as spatial domain, switching and wavelet filtering methods. These classes are based on the particular methodology used in image denoising algorithms and further subdivided to show their classification in terms of noise models utilized, application style, and stages the filters applied in images. In particular, we present a review of filtering methods in color image denoising, published over the past two decades. Our classification and succinct descriptions of color image restoration by these mathematical filtering techniques and their characterizations can help choose the appropriate ones for various downstream image processing tasks.

Anchor-Based Multi-Scale Deep Grasp Pose Detector With Encoded Angle Regression

An intelligent robot grasping system should be able to automatically grasp a variety of objects that have never been seen, which requires accurate and efficient grasp pose detection. To this end, we propose a deep grasp detector designed for the robot equipped with a parallel gripper. The deep model consumes RGB or depth data and extracts features via a feature pyramid network (FPN), followed by multiple grasp prediction units to output grasp parameters in a single stage without refining process. Attaching grasp prediction units to different FPN stages increases the model capability to predict different-size grasps. Furthermore, in each prediction unit, the grasp parameters are regressed with the horizontal anchor as a reference to overcome the challenges posed by the various shapes of the grasp regions. We improve the accuracy and efficiency of grasp rotation estimation by regressing the angle directly and encoding the angle with a continuous Gaussian-like curve during training. This encoded angle regression strategy provides distance information of different angle predictions without introducing additional computational costs. Evaluations on three datasets prove the superior performance of our method than state of the arts. The experiments in real scenarios further validate the effectiveness of our grasping system. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper proposes a robot system that can automatically grasp novel objects with a parallel gripper and RGB-D camera. We focus on generating accurate grasp configurations for various objects using the captured color or depth image, which is the cornerstone of a successful grasp. To obtain effective and efficient grasp pose detection, we present a deep model that generates robust grasp poses represented by rotated bounding boxes for multiple novel objects. The first step of the grasp detector is to capture the image features through a feature pyramid network (FPN). Then, we attach separate grasp prediction units to each layer of the FPN stage and adopt anchors as references to make the model robust to variable grasp rectangle sizes. In each grasp prediction unit, two separate subnetworks are used to directly output the grasp rectangles and their probabilities, without using an extra second stage to refine the predicted grasp areas. For the prediction of rotation angle, we encode the rectangle angles with a continuous Gaussian-like curve during training to improve the prediction accuracy. Our grasp detector is trained and tested on three datasets and validated on real-scene grasp experiments. Comparisons with state-of-the-art methods show that our model is more accurate while maintaining high efficiency. The proposed grasp detection model can be applied to generate stable grasps for novel objects with different shapes, colors, and materials. Our grasping system is capable of working in multiple scenarios, including homes, factories, and warehouses.

DWT versus DCT Based Hybrid Steganography and Watermarking Technique for Color Image Encryption.

DWT versus DCT Based Hybrid Steganography and Watermarking Technique for Color Image Encryption.

Lightweight mini-endoscope for two color imaging of neural activities with large field of view

We present the design and the experimental characterization of a miniature “portable” mini-endoscope with a large field-of-view (≈360rmmum) that operates with two spectral bands adapted for mCherry (peak emission at 610 nm) and GCaMP6s (peak emission at 513 nm) fluorophores. The diameter of the implant is very small (0.5 mm), the total weight is 1.8 g, the size is 10×12×21mm3, and the lateral resolution of the device is ≈4.5μm. This compact and lightweight system allows investigation of the neural activity of two different neuronal populations.

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.

Design of a logistics warehouse robot positioning and recognition model based on improved EKF and calibration algorithm

Automatic guided vehicles for logistics warehousing are a key link in the construction of intelligent logistics. To improve the positioning accuracy of warehouse robots, we designed an advanced extended Kalman filter method integrating multiple synchronous positioning techniques and map construction methods, and completed the calibration and detection of pallets based on color image information. The results revealed that the proposed multi-innovation enhanced model achieved minimum relative rotation and absolute trajectory errors of 0.13 and 0.09, outperforming existing models. It showcased excellent mapping fidelity and integrity (above 0.9) across various datasets, with a high loop detection success rate (0.91) enhancing map precision. The tray fusion detection algorithm's AUC (area under the curve) reached 0.92, reflecting a balanced accuracy-recall tradeoff. This research offers robust positioning and mapping capabilities in logistics warehousing environments, effectively identifying errors and ensuring pallet accuracy. The detection error and accuracy of this method are better than the other three models, with the lowest average absolute error of 0.32 and the lowest root-mean-square error of 0.27, and the overall error in the detection of pallets is small. The findings provide strong theoretical backing and technical support for advancing intelligent logistics warehousing technology. Precise positioning and identification capabilities enable logistics and warehousing robots to accurately and quickly complete tasks such as access, handling and sorting of goods, greatly improving the efficiency of warehousing operations, promoting the digital transformation and intelligent development of the logistics and warehousing industry, and improving the competitiveness of the industry and the level of service.

An approach for encrypting images using a four-dimensional logistic map

Encrypting image transmission has been one of the most complex challenges with communication technology ever. Millions of people utilize and share photographs via the internet for personal and professional purposes. One approach to secure picture transfer over the network is to use encryption techniques to transform the original picture into an unreadable or garbled version. Several novel and encouraging possibilities for creating secure Image data encryption techniques are presented by cryptographic algorithms grounded in chaotic logistic theory. Developing a foolproof method for encrypting both black-and-white and color image is the primary focus of this work. Both grayscale and color images can be encrypted using the keys generated by combining the chaotic logistic with the image's density in the first stage of the proposed system's encryption process. The original image can then be recovered in the second stage, which is the inverse of the encryption process. Two publicly available standard grayscale and color photographs were analyzed to evaluate the suggested approach. Peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI), and number of pixels change rate (NPCR) were found to be 6.6268, 51.3011, and 100, respectively, according to the test findings.

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.

Three Rounds Cryptography to Protect Secret Message

A simple three rounds method of message cryptography will be introduced, the message will be encrypted-decrypted by apply XORing operation of the message with the secret KEY1 in the first round, in the second round the message will be shuffled using the secret key KEY2, while in the third round the message binary matrix will be rotated left to a number of selected digits. The introduced method will be very secure it will use a secret kept digital color image as an image_key, this image will be used to generate KEY1 and KEY2. KEY1 will be extracted from the image from a selected position, while KEY2 will be an indices key obtained by sorting a number of bytes extracted from the image from another position determined by the user. In addition to the image_key the method will use the values of POS1, POS2, and NORLD to determine the starting position of key1, the starting position of key2 and the number of rotate left digits. The produced decrypted message will be very sensitive to the selected image_key, POS1, POS2 and NORTD. The encryption function will be simplified and it will be implemented using three simple tasks: XORing, shuffling and rotating left, while the decryption function will be implemented by applying rotate left, shuffling back and XORing operations. The proposed method will be implemented and tested using various messages, the results will be studied and analyzed to prove the achievements provided by the method in: quality, speed, security and sensitivity.

Enhancing Steganography in 256×256 Colored Images with U-Net: A Study on PSNR and SSIM Metrics with Variable-Sized Hidden Images

Enhancing Steganography in 256×256 Colored Images with U-Net: A Study on PSNR and SSIM Metrics with Variable-Sized Hidden Images

Digital Image Forgery Detection And Localization Using The Innovated U-Net

A reliable image copy–move forgery detection approach adaptable to different scenarios of tampering with color images is crucial for many applications. Different methods and solutions have been effectively proposed, but they are still subject to false positive/negative detections and cannot handle the variety of copy–move forgeries. In this paper, a machine learning model that combines ResNet 50 and U-net architectures for automatic image forgery detection in color image(s) is presented. The proposed system is inspired by the ResNet 50 architecture as an encoder and the U-Net architecture as a decoder. The encoder function implies applying convolution and normalizing for feature extraction. Conversely, the decoder functions is locating the spatial features. The decoder in the U-Net network comprises multiple decoder blocks, which are connected to corresponding encoder blocks by employing concatenate layers. A binary mask is then produced to represent the tampered regions in the image. Quantitative experimental results on two standard public datasets and a comparison with state-of-the-art methods demonstrate the effectiveness and robustness of the proposed model.

Developing the regression network generated from the classification network with deep learning

The field of image processing is very important in deep learning. One of the important applications in deep learning is the classification process after storing images in a data set, which requires training a deep neural network and a convolutional neural network (CNN) in MATLAB, because of the large amount of data carried by the color image, which leads to the need for deep learning technology, which leads to Discovering the number technology to create a matrix that includes the numbers from 0 to 9 in the 10 * 10 matrix, which leads to expanding the image store of the data set. In this work, a fast algorithm was created to create a large number matrix to store images, and the results that were achieved proved the efficiency of the algorithm.

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.

Identification of exfoliated monolayer hexagonal boron nitride films with a digital color camera under white light illumination

Optical microscopy with white light illumination has been employed when obtaining exfoliated monolayer hexagonal boron nitride (1L hBN) films from a large number of randomly placed films on a substrate. However, real-time observation of 1L hBN using a color camera under white light illumination remains challenging since hBN is transparent in the visible wavelength range. The poor optical constant of 1L hBN films in microphotographs is significantly improved using a Si substrate coated with a SiN x thin-film (SiN x /Si). When observing hBN thin films on SiN x /Si using a color digital camera in an optical microscope under white light illumination, the clarity of the captured color images depends on the thickness of the SiN x film (d). For real-time direct observation, the d was optimized based on quantitative chromatic studies tailored to Bayer filters of a color image sensor. Through image simulation, it was determined that the color difference between 1L hBN and the bare substrate is maximized at d = 59 or 70 nm, which was experimentally verified. The SiN x /Si with optimized d values visualized 1L hBN films without requiring significant contrast enhancement via image processing under white light illumination in real-time. Furthermore, the captured color photographs facilitate the reliable determination of the number of layers in few-layer hBN films using the contrast of the green channel of the images.

Full-colour-sorting metalens based on guided mode resonance

Full-colour-sorting metalens using few nanostructured layers have recently generated considerable interest as high-sensitivity colour image sensors because of their reliability and high resolution. Based on the guided mode resonance principle and propagation phase modulation, a full-colour-sorting metalens is designed, which can theoretically obtain any wavelength of light from the incident white light and realize arbitrary wavelength focusing with the same focal length in the visible range. Without the loss of generality, we propose a prototype of such metalens used for sorting blue, green and red light from the visible spectrum commonly used in practical applications. The maximum focussing efficiency is over 50% with a relatively high numerical aperture of 0.7. The proposed full-colour-sorting metalens can be used in many applications, such as colour mixing, colour holography, colour filtering, wavelength division multiplexing and so on.