Pixel Values Of Image Research Articles

A Novel GAN-based Chaotic Method with DNA Computing for Enhancing Security of Medical Images

Medical images are utilized to diagnose patients' health conditions. Nowadays, medical images are sent over the internet for diagnosis purposes. So, they should be protected from cyber attackers. These medical images are sensitive to any minor changes, and the data volume is rapidly increasing. Thus, security and storage costs must be considered in medical images. Traditional encryption and compression methods are ineffective for encrypting medical images due to their high execution time and algorithm complexity. In this paper, a novel 2D-chaos and Generative Adversarial Network (GAN) with DeoxyriboNucleic Acid (DNA) computing is proposed for generating encryption keys and improving the security of medical images. The proposed scheme uses GAN and 2D-chaos to generate the private key and diffusion process. The pixel values of the original images in the proposed schemes are shuffled using Mersenne Twister (MT) to improve the security of medical images. Moreover, the novel 2D-Chaotic Tent Map (2D-CTM) method is used to construct the key while performing XOR-based encryption. The proposed model has been tested on different medical images, namely the COVIDx-19 X-ray images, the malaria microscopic images, and the brain MRI images. The experiment results have been evaluated using performance metrics, namely key space, histogram analysis, entropy, key sensitivity, robustness analysis, correlation, SSIM, and MS-SSIM. The outcomes demonstrate that the proposed scheme is more effective than the state-of-the-art schemes.

Ground-Based Cloud Image Segmentation Method Based on Improved U-Net

Cloud image segmentation is a technique that divides images captured by meteorological satellites or ground-based observations into different regions or categories. By extracting the distribution, shape, and dynamic features of clouds, it provides precise data support for the meteorological and environmental fields, significantly influencing photovoltaic (PV) power generation forecasting, astronomical telescope observatory site selection, and weather forecasting. A ground-based cloud image segmentation model based on an improved U-Net is proposed, which adopts an overall encoder–decoder structure. In the encoder phase, this paper constructs a dilated convolution–atrous spatial pyramid pooling (ASPP)–dilated convolution structure to enhance early cloud feature extraction. Dilated convolution is a novel type of convolution that expands the receptive field by inserting holes into standard convolution, thereby capturing a larger range of contextual information. ASPP maintains high resolution while paying attention to both local details and global structures of the image. In the decoder stage, the bicubic interpolation method is used for up-sampling to restore the feature map resolution and improve the clarity of the segmented image. The bicubic interpolation method refers to the use of cubic polynomial functions to interpolate the pixel values of the input image. In addition, this paper designs a novel skip connection layer structure between the encoder and decoder, composed of a depthwise separable path (DS path) and an improved channel spatial attention module (Im-CSAM) connected in sequence. The DS path combines depthwise separable convolutions and residual structures to facilitate information exchange between high-level and low-level features. The Im-CSAM is a modular attention mechanism that focuses on important cloud features in spatial and channel dimensions to enhance segmentation accuracy. Experiments show that compared to the traditional U-Net, the accuracy, precision, and MIoU of this model improved by 2.2%, 4.1%, and 5.0%, respectively, in the SWINySEG dataset, and by 3.2%, 3.6%, and 5.8%, respectively, in the TCDD dataset, proving that the improved method has a better generalization ability and segmentation performance.

An efficient image encryption algorithm using 3D-cyclic chebyshev map and elliptic curve

With the development of internet technologies, it is now usual to communicate enormous amounts of text and visual data over networks, which calls for stringent procedures to guarantee confidentiality and integrity throughout transmission. An important part of safeguarding image communication over secure channel is cryptography. This study proposes an effective way to secure sensitive data by creating tamper-proof cryptosystems and authentication mechanisms. By employing elliptic curve cryptography (ECC) to generate secret encryption key and chaotic maps to successfully disguise and alter plain image pixel values, the technique strengthens overall system security through the use of hybrid cryptography. The method has a big enough key space to withstand brute-force attempts and exhibits robustness against statistical and differential attacks through analysis including evaluations and assessments of correlation, entropy, and histograms as well as evaluations such as NPCR, UACI, PSNR and keyspace analysis validate the efficacy of the proposed image encryption algorithm, affirming its systematic and robust nature and its capacity to offer superior image encryption protection over some existing ones.

Research on cloud dynamic public key information security based on elliptic curve and primitive Pythagoras

In order to solve the problems of key redundancy, transmission and storage security and the difficulty of realizing the one-time-secret mode in image encryption algorithm, this paper proposes a cloud dynamic public key information security scheme based on elliptic curve and primitive Pythagoras. The 6-D Lorentz hyperchaos system is used as the key generator, and the generated key is stored in the cloud key management center, which supports automatic update and dynamic change to further ensure the security of the key. The encryption key is selected by elliptic curve and primitive Pythagorean triplet. In the scrambling algorithm, according to the parity of random number and the parity of image pixel value coordinates, elliptic curve is used to reset the plaintext image. In the diffusion algorithm, the three-side rotation characteristic of the Rubik’s cube is simulated, the binary sequence of pixel values is stored at eight vertices of the cube, the cube is rotated according to the key value, and the changed 8 new binary numbers are XOR operated with the random sequence to obtain the new pixel value. The algorithm in this paper is applied to network transmission, which can effectively prevent data eavesdropping, tampering and forgery, ensure communication security.

Simulating images of radio galaxies with diffusion models

Context. With increasing amounts of data produced by astronomical surveys, automated analysis methods have become crucial. Synthetic data are required for developing and testing such methods. Current classical approaches to simulations often suffer from insufficient detail or inaccurate representation of source type occurrences. Deep generative modeling has emerged as a novel way of synthesizing realistic image data to overcome those deficiencies. Aims. We implemented a deep generative model trained on observations to generate realistic radio galaxy images with full control over the flux and source morphology. Methods. We used a diffusion model, trained with continuous time steps to reduce sampling time without quality impairments. The two models were trained on two different datasets, respectively. One set was a selection of images obtained from the second data release of the LOFAR Two-Metre Sky Survey (LoTSS). The model was conditioned on peak flux values to preserve signal intensity information after re-scaling image pixel values. The other, smaller set was obtained from the Very Large Array (VLA) survey of Faint Images of the Radio Sky at Twenty-Centimeters (FIRST). In that set, every image was provided with a morphological class label the corresponding model was conditioned on. Conditioned sampling is realized with classifier-free diffusion guidance. We evaluated the quality of generated images by comparing the distributions of different quantities over the real and generated data, including results from the standard source-finding algorithms. The class conditioning was evaluated by training a classifier and comparing its performance on both real and generated data. Results. We have been able to generate realistic images of high quality using 25 sampling steps, which is unprecedented in the field of radio astronomy. The generated images are visually indistinguishable from the training data and the distributions of different image metrics were successfully replicated. The classifier is shown to perform equally well for real and generated images, indicating strong sampling control over morphological source properties.

Research on Key Technologies of Spatial Domain Image Steganography and Analysis of Typical Applications

In the digital age, secure storage and communication of information are crucial aspects of everyday life, drawing significant global interest. Steganography serves as a pivotal method for concealing private information within various media forms—such as images, videos, and text—enabling secure information transmission. This paper delineates the distinctions between information hiding and encryption and delves into image steganography. The focus here is on spatial-based image steganography techniques, which are broadly categorized into traditional and deep learning-based methods. Traditional methods rely on modifications to the pixel values of the host image, often resulting in minor but detectable changes if not carefully managed. On the other hand, deep learning-based methods leverage neural networks to learn how to encode data in an image in a way that is much harder to detect. A comprehensive performance analysis of these techniques is provided, complete with comparative tables to aid in clarity and reference. Additionally, this paper aims to guide researchers by charting current trends in the field and suggesting future research directions. By exploring these advanced techniques, the paper contributes to the broader discourse on enhancing the security and efficacy of steganographic practices.

A Quantum Image Secret Sharing Scheme Based on Designated Multi‐Verifier Signature

AbstractThis paper presents a quantum image secret sharing scheme based on designated multi‐verifier signature. First, share images are generated by a 2D hyperchaotic system incorporating sinusoidal mapping, Henon mapping, and Cubic mapping using the cascade modulation method. Quantum Arnold scrambling and diffusion are performed on the secret image. Subsequently, the concept of designated multi‐verifier signature is introduced. In the secret image sharing phase, the pixel values of the share images are used to sign the secret image, and the signed secret image and share images are sent to a trusted third party and designated verifiers, respectively. In this scheme, no carrier images are required, and the share images held by the participants do not contain any information about the secret image. Finally, simulation experiments and security analysis are conducted on IBM Qiskit platform and Matlab software.

A novel image encryption method based on the cycle replacement

For the bit-level image encryption algorithms, pixel values and positions can be changed simultaneously. The operation can enhance the security of image encryption but will require the complicated calculations. Therefore, high security and suitable computation for a new algorithm are needed to be considered. In this paper, a novel image encryption algorithm, which combines the bit-level encryption and the pixel-level encryption methods, is proposed based on the cycle replacement. Firstly, a new 2-dimensional (2D) map with a hyperbolic cosine function (2D-Cosh map) is introduced, which has rich and complex dynamics. Based on the chaotic characteristic of the map, an image encryption algorithm is introduced via the substitution of bit of pixels which can scramble the pixels, and change the image pixel positions effectively. Numerical simulation and security analysis are used to demonstrate the effectiveness and feasibility of the algorithm. From which we can see that the correlation coefficients are almost 0, average entropy = 7.9973, average NPCR = 99.6104%, and average UACI = 33.4664%. It is clear that the algorithm is resistant to differential attacks, interference attacks, and can reduce the correlation of adjacent pixels of the encrypted image greatly. Meanwhile, the algorithm has no limit for the size of a color image in the process of the encryption.

Analysis of Rock Mass Weathering Grade Using Image Processing Technique

Image processing techniques refer to the process of converting an image into a digital format and then performing various operations on it to extract useful information. In this study, image processing technique has been used to categorize rock masses according to its weathering grades. The pixel values of the sample images were in the form of RGB color space before being converted to CIELAB color space. The conversion uses ° as the illuminant. The value in CIELAB color space represents the green-red opponent colors, with negative values for green and positive values for red. In contrast, the value represents the blue-yellow opponents with negative values for blue and positive values for yellow. From the values of and of the samples, clustering was used to classify the samples. This method will group the and values into seven clusters according to the closest distance between the values and the centroids. The proposed study can differentiate between the rock mass and rejected clusters containing plants and painted numbers on the rock. The painted number is placed in a rejected cluster due to the inability to determine the exact color of the rock, thereby impacting the data accuracy. The results have been discussed, and the rock masses have been categorized based on weathering grade. Several limitations have been identified, such as the presence of shadows in the sample images and the lack of arrangement of outcome images according to their a* and b* values. This research has also been validated and compared with previous studies. The JudGeo software utilized in prior research required human input to manually estimate suitable a* and b* values, whereas the proposed method automatically computes these values during color space conversion of the sample. Additionally, the proposed method can calculate the percentage of each cluster, facilitating the classification of rock mass into its respective weathering grade.Image processing techniques refer to the process of converting an image into a digital format and then performing various operations on it to extract useful information. In this study, image processing technique has been used to categorize rock masses according to its weathering grades. The pixel values of the sample images were in the form of RGB color space before being converted to CIELAB color space. The conversion uses ° as the illuminant. The value in CIELAB color space represents the green-red opponent colors, with negative values for green and positive values for red. In contrast, the value represents the blue-yellow opponents with negative values for blue and positive values for yellow. From the values of and of the samples, clustering was used to classify the samples. This method will group the and values into seven clusters according to the closest distance between the values and the centroids. The proposed study can differentiate between the rock mass and rejected clusters containing plants and painted numbers on the rock. The painted number is placed in a rejected cluster due to the inability to determine the exact color of the rock, thereby impacting the data accuracy. The results have been discussed, and the rock masses have been categorized based on weathering grade. Several limitations have been identified, such as the presence of shadows in the sample images and the lack of arrangement of outcome images according to their a* and b* values. This research has also been validated and compared with previous studies. The JudGeo software utilized in prior research required human input to manually estimate suitable a* and b* values, whereas the proposed method automatically computes these values during color space conversion of the sample. Additionally, the proposed method can calculate the percentage of each cluster, facilitating the classification of rock mass into its respective weathering grade.

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

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).

Application of an ensemble CatBoost model over complex dataset for vehicle classification.

The classification of vehicles presents notable challenges within the domain of image processing. Traditional models suffer from inefficiency, prolonged training times for datasets, intricate feature extraction, and variable assignment complexities for classification. Conventional methods applied to categorize vehicles from extensive datasets often lead to errors, misclassifications, and unproductive outcomes. Consequently, leveraging machine learning techniques emerges as a promising solution to tackle these challenges. This study adopts a machine learning approach to alleviate image misclassifications and manage large quantities of vehicle images effectively. Specifically, a contrast enhancement technique is employed in the pre-processing stage to highlight pixel values in vehicle images. In the feature segmentation stage, Mask-R-CNN is utilized to categorize pixels into predefined classes. VGG16 is then employed to extract features from vehicle images, while an autoencoder aids in selecting features by learning non-linear input features and compressing representation features. Finally, the CatBoost (CB) algorithm is implemented for vehicle classification (VC) in diverse critical environments, such as inclement weather, twilight, and instances of vehicle blockage. Extensive experiments are conducted using different large-scale datasets with various machine learning platforms. The findings indicate that CB (presumably a specific method or algorithm) attains the highest level of performance on the large-scale dataset named UFPR-ALPR, with an accuracy rate of 98.89%.

Can processed images be used to determine the modulation transfer function and detective quantum efficiency?

The modulation transfer function (MTF) and detective quantum efficiency (DQE) of x-ray detectors are key Fourier metrics of performance, valid only for linear and shift-invariant (LSI) systems and generally measured following IEC guidelines requiring the use of raw (unprocessed) image data. However, many detectors incorporate processing in the imaging chain that is difficult or impossible to disable, raising questions about the practical relevance of MTF and DQE testing. We investigate the impact of convolution-based embedded processing on MTF and DQE measurements. We use an impulse-sampled notation, consistent with a cascaded-systems analysis in spatial and spatial-frequency domains to determine the impact of discrete convolution (DC) on measured MTF and DQE following IEC guidelines. We show that digital systems remain LSI if we acknowledge both image pixel values and convolution kernels represent scaled Dirac -functions with an implied sinc convolution of image data. This enables use of the Fourier transform (FT) to determine impact on presampling MTF and DQE measurements. It is concluded that: (i)the MTF of DC is always an unbounded cosine series; (ii)the slanted-edge method yields the true presampling MTF, even when using processed images, with processing appearing as an analytic filter with cosine-series MTF applied to raw presampling image data; (iii)the DQE is unaffected by discrete-convolution-based processing with a possible exception near zero-points in the presampling MTF; and (iv)the FT of the impulse-sampled notation is equivalent to the transform of image data.

Color-to-Gray Conversion Method Based on Chroma Difference

Most of the images encountered in daily life are color images, yet grayscale remains prevalent in many fields due to its reduced data size and simplified operations. When reducing the dimensions of a three-channel color image to a single-channel grayscale, a portion of the original color information is inevitably lost. To yield high-quality grayscale images, this study introduces a color-to-gray conversion method based on chroma difference. This method defines a novel color distance metric for color-to-grayscale conversion, incorporating pixel chromaticity differences alongside brightness variations. The discrepancy in gray pixel values in the output grayscale image effectively mirrors the overall differences among input color image pixels. Optimization is conducted using the conjugate gradient method, ensuring appropriate reflection of luminance information from the input image and chromaticity data from the original color image within the grayscale rendering. Experimental validation confirms the efficacy of this approach. However, as the method accounts for all pixel pairs, it occasionally considers unnecessary pairs, leading to potential distortion in color differences between pixels and consequent inadequacies in chromaticity variation. To address this issue, a weighting factor is introduced, prioritizing color combinations with similar color differences. Experimental findings demonstrate that grayscale images produced using the proposed method outperform those generated by alternative approaches in preserving color differentials and retaining detailed features of the original image. The resulting output exhibits clear and natural outlines, as supported by both subjective and objective assessments.

Integration of LST and NDVI for adjusted night light index (ILNANLI): a novel index to reduce the effect of saturation and blooming of DMSP-OLS data

ABSTRACT DMSP-OLS night-light images have provided an effective way to monitor cities on a global scale. These images can separate urban areas and other human activities from the background without light with correct spatial measurement. However, due to saturation and blooming problems, the ability of these images to describe urban features is limited, especially in urban cores. Therefore, in this research, a new index, ILNANLI, has been developed and presented by integrating LST and NDVI for the adjusted night-light index. This index has two general goals: reducing the effects of saturation in the city centres, increasing the variety of brightness pixel values of night-light images, and reducing the blooming in the surrounding areas. The ILNANLI has been developed in such a way that it has put the indicators and relationships together, taking into account the characteristics of different regions of the city (such as buildings, vegetation, and soil) in such a way that in every urban environment (temperate and dry and semi-desert) to be successful. Another feature is its easy implementation and high success. In fact, the prominent feature of this index, which distinguishes it from the rest of the indices, is that it has been able to work successfully in arid and semi-desert areas in addition to temperate regions. The evaluation results showed that the average overall accuracy of ILNANLI in mapping and extracting built-up areas with automatic thresholds in cities with different climates has effectively increased by 8% compared to other indexes. Also, the examination of transects shows that the proposed index is more effective than the other four indexes in increasing the diversity of values and reducing blooming. Therefore, as a favourable option with easy implementation, the proposed index can be used to reduce the effect of saturation and blooming and study the urban structure at local scales.

New 4D hyperchaotic system’s application in image encryption

In order to protect sensitive information from unauthorized access and illegal copy during network transmission, storage and processing, we propose a new four-dimensional hyperchaotic system (4DHS) and apply it to encryption algorithm. Firstly, the dynamical properties of 4DHS are analyzed according to the structure, and the chaotic properties are verified by dissipation, equilibrium point and lyapunov exponent. Secondly, the chaotic sequence combined with Arnold scrambling method is adopted to scramble the pixel values of the plaintext image, and the scrambled pixel matrix is diffused into the ciphertext image matrix by XOR operation. Finally, we conduct the experiments to validate the effectiveness of the proposed encryption algorithm and achieve satisfactory results. At the same time, we compare the proposed encryption algorithm with other encryption algorithms, and the excellent encryption effect of our encryption algorithm can be proved.

Applying High‐Speed Video Images to Inverse Channel Base Current Based on NARX Neural Network

AbstractThe high‐speed video (HSV) images were taken as the input data of the nonlinear autoregressive network (NARX) to reconstruct the return‐stroke channel base current (CBC). The integrated luminosity (IL) of discharge channel is firstly obtained by summing the pixel values in HSV images, and the correlation between the IL and CBC is analyzed as well. Then, a NARX model is trained by taking the IL and CBC as the input and output data, respectively. With the input of the IL of another discharge channel, the trained NARX model is applied to inverse the corresponding CBC. The inversed result suggests that the proposed method can be used to infer the CBC that was not yet measured.

Dynamic correction of dual−energy x-ray absorptiometry images improves chain speed prediction of lamb composition in abattoirs

A prototype, on-line Dual Energy X-ray Absorptiometer (DXA) has shown high precision of the prediction of carcass composition for the purpose of improved sheep meat grading in the Australian lamb supply chain, albeit with small inaccuracies over time. These inaccuracies were present across hours, and more significantly across days, which were unacceptable for any accreditation of this device as an objective carcass measurement tool in Australia. This inaccuracy demanded the creation of a novel image−processing algorithm for the prototype DXA. This DXA was tested for repeatability of predictions of lamb carcass composition over minutes, hours, and days, using two developed image processing algorithms. There was high immediate repeatability for both algorithms when predicting lean muscle % in 40 lamb carcasses, with a maximum CV of 0.65% over five repeated scans. There was a decrease in the CV of the prediction of lean muscle % of 30 lambs scanned three times over a 48-h period from 5.93 to 1.19% when the superior algorithm was used. The inaccuracies of lean muscle % predictions were associated with increases in the unattenuated space pixel values in DXA images. Improvements of the current algorithm are required to demonstrate repeatability over time for the purpose of accreditation within the Australian sheep meat industry, and for possible expansion of this technology into international supply chains.

A subway tunnel image stitching method based on point cloud mapping relationships and high-resolution image

The paper presents an intelligent tunnel 3D laser image acquisition system. The system can quickly acquire the 3D point cloud and tunnel surface image of the whole tunnel section. The paper proposes the subway tunnel image stitching method based on point cloud mapping relationships and high-resolution image. Firstly, the method extracts the homonymous points from the mapped image generated from the 3D point cloud and the camera image based on the maximum information coefficient method. Secondly, the 3D point cloud positions corresponding to the homonymous points are determined. The optimization of camera parameters is achieved based on the gradient descent method. Then, the tunnel point cloud is expanded to generate a gray scale image based on spherical projection. Based on the optimized parameters, the point cloud is projected to the camera image to determine the depth value of each camera image pixel point. Finally, the pixel values of the camera image are projected based on the spherical projection to obtain the stitching result images. This method stitches individual images together to form a more complete tunnel surface image. The larger tunnel surface image helps in identifying tunnel defects.

Intelligent fault diagnosis of railway pantograph using a novel graph construction methodology

Railway pantographs provide power for railway vehicles by conducting electrical energy from overhead catenary. The failure of the pantograph tends to damage the contact quality between the pantograph and the catenary, reducing the transmission efficiency of electric energy. Hence, fault diagnosis of pantograph plays a significant role in expanding the service life of railway vehicles. In this work, a novel graph construction method is proposed for the fault diagnosis of pantographs combined with a graph neural network (GNN). In the graph construction method, 1D load signals collected from the test pantograph are firstly transformed into multiple 2D images with the same size in both time and frequency domains using Gramian angular field, Markov transition field and recurrence plot. Secondly, pixel values in images are regarded as features in vertexes of graphs, and graphs can be constructed by connecting neighbor vertexes. Finally, the GNN model is trained by constructed graphs for obtaining the fault diagnosis model of pantographs. Laboratory experiments are implemented to show the advantages of the proposed method by comparing it with other conventional methods.

Hue Channel Mesoscale Convective System Identification Techniques using Kalpana Geostationary Satellite Observations

Climate change is leading to sudden extreme weather events like floods and heavy rain to occur. One of the most significant rain-bearing systems, Mesoscale Convective Systems (MCSs), is in charge of catastrophic rainfall and flood events that may cause loss of life and property. MCSs are one of the vital components of the climate system on Earth. Many studies contributed to two significant steps, MCS identification and tracking. There are a variety of algorithms focused on tracking whereas for MCS identification only limited methods are present. They contribute to global as well as regional climate patterns by transporting heat and moisture. It is necessary to identify MCS properly to track MCS occurrences over time and comprehend their typical lifecycle to get early warnings of their existence. Three MCS identification techniques based on the Hue channel of the Hue Saturation Value (HSV) color model are implemented in this research, and their effectiveness is assessed. These techniques execute segmentation based on Hue channel Thresholding (HT), K means clustering combined with Hue channel Thresholding (KMCHT) and the modified Source Apportionment Technique combined with Hue channel Thresholding (SATHT). Image pixel values are used to depict infrared brightness temperature data obtained from the Indian geostationary satellite Kalpana-1. The generated ground truth images and performance measurements are used to assess the effectiveness of the methods. The proposed SATHT method for multiple cloud segmentation results in superior performance metrics than the HT and KMCHT approaches.