Pixel Of Color Image Research Articles

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.

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.

Research on interference compensation methods for color image sensors based on iterative learning

PurposeFixed mode noise and random mode noise always exist in the image sensor, which affects the imaging quality of the image sensor. The charge diffusion and color mixing between pixels in the photoelectric conversion process belong to fixed mode noise. This study aims to improve the image sensor imaging quality by processing the fixed mode noise.Design/methodology/approachThrough an iterative training of an ergoable long- and short-term memory recurrent neural network model, the authors obtain a neural network model able to compensate for image noise crosstalk. To overcome the lack of differences in the same color pixels on each template of the image sensor under flat-field light, the data before and after compensation were used as a new data set to further train the neural network iteratively.FindingsThe comparison of the images compensated by the two sets of neural network models shows that the gray value distribution is more concentrated and uniform. The middle and high frequency components in the spatial spectrum are all increased, indicating that the compensated image edges change faster and are more detailed (Hinton and Salakhutdinov, 2006; LeCun et al., 1998; Mohanty et al., 2016; Zang et al., 2023).Originality/valueIn this paper, the authors use the iterative learning color image pixel crosstalk compensation method to effectively alleviate the incomplete color mixing problem caused by the insufficient filter rate and the electric crosstalk problem caused by the lateral diffusion of the optical charge caused by the adjacent pixel potential trap.

AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models.

Color face images are often transmitted over public channels, where they are vulnerable to tampering attacks. To address this problem, the present paper introduces a novel scheme called Authentication and Color Face Self-Recovery (AuCFSR) for ensuring the authenticity of color face images and recovering the tampered areas in these images. AuCFSR uses a new two-dimensional hyperchaotic system called two-dimensional modular sine-cosine map (2D MSCM) to embed authentication and recovery data into the least significant bits of color image pixels. This produces high-quality output images with high security level. When tampered color face image is detected, AuCFSR executes two deep learning models: the CodeFormer model to enhance the visual quality of the recovered color face image and the DeOldify model to improve the colorization of this image. Experimental results demonstrate that AuCFSR outperforms recent similar schemes in tamper detection accuracy, security level, and visual quality of the recovered images.

Color UAV Image Edge Detection Based on Improved Fireworks Algorithm

Image edge detection plays a crucial role in image analysis and recognition. However, when dealing with color images captured by unmanned aerial vehicles (UAVs), there are certain limitations, such as large operations, multiple noise sources, easy distortion, and missing information in edge detection. To address these shortcomings, this study proposes a UAV color image edge detection method based on an enhanced fireworks algorithm. In this method, the color image pixels of the UAV are represented using quaternions. The explosion amplitude formula of the fireworks is divided into two categories based on the mean value of the number of fireworks explosions. For each category, an explosion formula is proposed, and the explosion mutation operator of the fireworks algorithm is improved accordingly. By applying the proposed algorithm, the preliminary edges of a UAV color image are obtained. Additionally, a novel approach for color image edge refinement is introduced. This approach involves classifying the edge points based on their degree of attachment, which leads to the formation of the edges in a UAV color image. Experimental results demonstrate that the algorithm proposed in this study offers several advantages, including fast calculation, strong denoising capability, and high-quality edge detection.

Multi-phase FZA lensless imaging via diffusion model.

Lensless imaging shifts the burden of imaging from bulky and expensive hardware to computing, which enables new architectures for portable cameras. However, the twin image effect caused by the missing phase information in the light wave is a key factor limiting the quality of lensless imaging. Conventional single-phase encoding methods and independent reconstruction of separate channels pose challenges in removing twin images and preserving the color fidelity of the reconstructed image. In order to achieve high-quality lensless imaging, the multiphase lensless imaging via diffusion model (MLDM) is proposed. A multi-phase FZA encoder integrated on a single mask plate is used to expand the data channel of a single-shot image. The information association between the color image pixel channel and the encoded phase channel is established by extracting prior information of the data distribution based on multi-channel encoding. Finally, the reconstruction quality is improved through the use of the iterative reconstruction method. The results show that the proposed MLDM method effectively removes the influence of twin images and produces high-quality reconstructed images compared with traditional methods, and the results reconstructed using MLDM have higher structural similarity and peak signal-to-noise ratio.

Color Image Edge Detection Method Based on the Improved Whale Optimization Algorithm

A high level of computation is required for edge detection in color images captured by unmanned aerial vehicles (UAVs) to address issues, such as noise, distortion, and information loss. Thus, an edge detection method for UAV-captured color images based on the improved whale optimization algorithm (WOA) is proposed in this study. In this method, the color image pixels are represented by quaternions, and the global random position variables and information exchange mechanism are introduced into the random walk foraging formula of the WOA. Further, a random disturbance factor is also introduced into the predator-prey formula of the spiral bubble net. The proposed improved WOA is then used to obtain the preliminary edge of the UAV-captured color image. An edge-point classification method using the radius of the shortest distance between the whale and the current global optimum in each iteration is presented to enhance a preliminary edge. The experimental results show that the proposed edge detection method has the advantages of strong denoising, fast speed, and good quality.

Color image encryption based on 2D enhanced hyperchaotic logistic-sine map and two-way Josephus traversing

To improve the chaotic behavior of the chaotic map, this paper proposes a new two-dimensional (2D) chaotic map, namely, 2D enhanced hyperchaotic Logistic-sine map (2D-EHLM), which is derived from the idea of the 2D Logistic map and the Sine map. The performance analyses demonstrate that the 2D-EHLM has significantly better hyperchaotic property, unpredictability characteristics in terms of their Lyapunov exponents, trajectories, and 0-1test in comparison to some existing 2D chaotic maps. Such as the 2D-EHLM performs chaotic behavior when their control parameter μ within (−3,4), which indicates a large chaotic range comparing with other 2D dynamical systems. To investigate its application, this paper proposes a new image confusion strategy based on the two-way Josephus traversing (TJT) to efficiently scramble the color image components and pixel positions. Combining 2D-EHLM and TJT operation, a new color image encryption scheme is proposed. The stimulation experiments and security analysis demonstrate that the proposed algorithm is able to protect the image information with a high security as well as to resist common types of attacks, which also verify the superior hyperchaotic behaviors of the 2D-EHLM from the aspect of encryption effect.

Color Image Feature Matching Method Based on the Improved Firework Algorithm

An improved ORB feature point purification method has been proposed to address the problem of large feature point matching error and low image registration rate in the oriented FAST and rotated BRIEF (ORB) feature point color image matching algorithm. Pure virtual quaternions were used to represent the color image pixels in this method, and an improved FAST algorithm was used to detect the color feature points at first. The firework algorithm has been used to divide the detected feature points into key areas, auxiliary areas, and small influence areas, depending on the degree of attachment. The feature points of the key area and the subsidiary area are the required feature points. In order to further improve the efficiency of matching the color image feature points, the firework explosion radius formula and the explosion number formula in the firework algorithm have been improved, and an improved firework algorithm is proposed. This improved algorithm purifies the quaternion-represented color image feature points. For feature matching, the hamming distance was used. Experiments show that, when compared to the traditional ORB algorithm, the improved algorithm retains the key feature points of the color image with high accuracy, removes a large number of irrelevant feature points and noise points, and provides significantly higher accuracy and efficiency of color image matching.

IEHC: An efficient image encryption technique using hybrid chaotic map

Image encryption is essential for the transmission of the sensitive images through insecure channels used in wired or wireless communication nowadays. With the advent of cloud enabled surveillance applications, Internet of Things applications, social media communications, the requirement of image encryption is also increasing day by day. The conventional encryption schemes demand high resources in terms of processing power, memory and bandwidth resulting degradation in overall system response time. In this paper, first a novel shuffling technique has been introduced to shuffle the pixels of an input color image. Subsequently, this paper proposes an image cipher that uses Logistic map and Arnold's cat map for encryption of color images. The experimental results and various security analyses exhibit good security characteristics and also prove its robustness against various types of noises, data loss and cryptanalysis attacks, making it appropriate for implementing in real-time applications.

Spatially Consistent Transformer for Colorization in Monochrome-Color Dual-Lens System.

We study the colorization problem in monochrome-color dual-lens camera systems, i.e. colorizing the gray image from the monochrome camera using the color image from the color camera as reference. In related methods, cost volume based CNN methods achieve the state-of-the-art results, but they are costly in GPU memory due to building the 4D cost volume. Recently, some slice-wise cross-attention based methods are proposed for related problems. The slice-wise cross-attention has much less costs in GPU memory but directly using them for this colorization problem cannot generate competing results. We make use of the non-local computation property of cross-attention to propose a transformer based method. To overcome the limitations of straight-forward slice-wise cross-attention, we propose the spatially consistent cross-attention (SCCA) block to encourage pixels of slices across different epipolar lines in the gray image to find spatially consistent correspondence with pixels of the reference color image. And, to further reduce the memory cost while keeping the colorization accuracy, we design a pyramid processing strategy to cascade a series of SCCA blocks with smaller slice size and perform the colorization from coarse to fine. To extract more powerful image features, we use several regional self-attention (RSA) blocks with U-style connections. Experimental results show that we outperform the state-of-the-art methods largely on the synthesized datasets of Cityscapes, Sintel, and SceneFlow, and the real monochrome-color dual-lens dataset.

English

In order to achieve the multi-focus image fusion task, a sparse representation method based on quaternion for multi-focus image fusion is proposed in this paper. Firstly, the RGB color information of each pixel in the color image is represented by quaternion based on the relevant knowledge of computational mathematics, and the color image pixel is processed as a whole vector to maintain the relevant information between the three color channels. Secondly, the dictionary represented by quaternion and the sparse coefficient represented by quaternion are obtained by using the our proposed sparse representation model. Thirdly, the coefficient fusion is carried out by using the “max-L1” rule. Finally, the fused sparse coefficient and dictionary are used for image reconstruction to obtain the quaternion fused image, which is then converted into RGB color multi-focus fused image. Our method belongs to computational mathematics, and uses the relevant knowledge in the field of computational mathematics to help us carry out the experiment. The experimental results show that the method has achieved good results in visual quality and objective evaluation.

Semi-public watermarking digital implementation in the concealment of text messages

The purpose of this study is to generate watermarking of an input image. The watermarking process will begin with the process of reading color image pixels. After that, the process will be continued by calculating the step message value inserted and inserting all the necessary values in the watermarking checking process. In the event of an attack, then at the time of watermarking formation, a specific noise value (randomly generated) will be calculated to be inserted into the image. After the process of watermarking formation, then the process can be continued with checking watermarking and extracting messages.

Normal manipulation for bas-relief modeling

We introduce a normal-based modeling framework for bas-relief generation and stylization which is motivated by the recent advancement in this topic. Creating bas-relief from normal images has successfully facilitated bas-relief modeling in image space. However, the use of normal images in previous work is restricted to the cut-and-paste or blending operations of layers. These operations simply treat a normal vector as a pixel of a general color image. This paper is intended to extend normal-based methods by processing the normal image from a geometric perspective. Our method can not only generate a new normal image by combining various frequencies of existing normal images and details transferring, but also build bas-reliefs from a single RGB image and its edge-based sketch lines. In addition, we introduce an auxiliary function to represent a smooth base surface or generate a layered global shape. To integrate above considerations into our framework, we formulate the bas-relief generation as a variational problem which can be solved by a screened Poisson equation. One important advantage of our method is that it can generate more styles than previous methods and thus it expands the bas-relief shape space. We experimented our method on a range of normal images and it compares favorably to other popular classic and state-of-the-art methods.

A Combined Sudoku and Synthetic Colour Image Techniques for Cryptographic Key Generation

The exchange of sensitive digital information has increased significantly in the last decade due to the internet’s growth and availability. More robust encryption techniques were introduced to secure the data-in-transit and data-at-rest. The strength of these encryption algorithms depends on the keys. Keys are generated using Pseudorandom Number Generators (PRNG). The key generation algorithm defines these PRNGs. Although there are many robust key generation algorithms, there is a demand for new key generation algorithms to break hackers’ experience and knowledge. This paper proposed a new key generation algorithm using value generated from sudoku matrices and synthetic color image. 8 × 8 sudoku matrices are generated randomly for each session. These sudoku matrices are solved using backtracking and recursive programming. The solved sudoku matrices are used to extract bits from pixels of the synthetic color image. The extracted bits are then combined to form a key. Further, randomness in the key is tested using the NIST (National Institute of Standards and Technology).

An advanced image compression technique by using a coupled compression algorithms depend on different wavelet methods

Digital images need a large storage capacity, and as a result they need a large bandwidth for data transmission to deliver to the desired destination over the network. Image compression technologies not only reduce the size of stored data, but also maintain as much as possible the output image quality. In the proposed research we review a technique for image compression that uses a distinct two-stage image encoding method using different compression algorithms and wavelet transform methods, which combines two types of effective compression algorithms that give more ability to compress image data. The proposed compression technique which coupled two image compression algorithms that put to use premium characteristics from each algorithm. The wavelet transform methods contribute effectively to finding suitable solutions to supply better compression ratios for images with high resolution. The complete series of compression includes repeated stages of encoding and decoding, in addition to the wavelet processing itself. This study will have carried out an advanced compression technique that contain a coupled compression algorithms relying on the preferred wavelets to this work from practical experiments they are, biorthogonal and Haar wavelet transform, the performance metrics for tested true HD color image will be studied. The challenge for image compression algorithms is to detect a best solution between a low compression ratio and good visual perception results. An essential measure of achieved image compression process is taken by compression ratio CR and the ratio of bit-per-pixel BPP. The CR and BPP metrics are important components in image compression techniques. Through the results of the image compression metrics in two stages, the best practical results were obtained when the compression ratio metric CR was equal to 2.3%, and this metric indicates that the compressed image can be stored using 2.3% of the original image data size. While the BPP which represent the bit number that used to store one pixel of true color image is equal to 0.575.

An advanced image compression technique by using a coupled compression algorithms depend on different wavelet methods

Digital images need a large storage capacity, and as a result they need a large bandwidth for data transmission to deliver to the desired destination over the network. Image compression technologies not only reduce the size of stored data, but also maintain as much as possible the output image quality. In the proposed research we review a technique for image compression that uses a distinct two-stage image encoding method using different compression algorithms and wavelet transform methods, which combines two types of effective compression algorithms that give more ability to compress image data. The proposed compression technique which coupled two image compression algorithms that put to use premium characteristics from each algorithm. The wavelet transform methods contribute effectively to finding suitable solutions to supply better compression ratios for images with high resolution. The complete series of compression includes repeated stages of encoding and decoding, in addition to the wavelet processing itself. This study will have carried out an advanced compression technique that contain a coupled compression algorithms relying on the preferred wavelets to this work from practical experiments they are, biorthogonal and Haar wavelet transform, the performance metrics for tested true HD color image will be studied. The challenge for image compression algorithms is to detect a best solution between a low compression ratio and good visual perception results. An essential measure of achieved image compression process is taken by compression ratio CR and the ratio of bit-per-pixel BPP. The CR and BPP metrics are important components in image compression techniques. Through the results of the image compression metrics in two stages, the best practical results were obtained when the compression ratio metric CR was equal to 2.3%, and this metric indicates that the compressed image can be stored using 2.3% of the original image data size. While the BPP which represent the bit number that used to store one pixel of true color image is equal to 0.575.

Quantitative Cluster Headache Analysis for Neurological Diagnosis Support Using Statistical Classification

Cluster headache (CH) belongs to the group III of The International Classification of Headaches. It is characterized by attacks of severe pain in the ocular/periocular area accompanied by cranial autonomic signs, including parasympathetic activation and sympathetic hypofunction on the symptomatic side. Iris pigmentation occurs in the neonatal period and depends on the sympathetic tone in each eye. We hypothesized that the presence of visible or subtle color iris changes in both eyes could be used as a quantitative biomarker for screening and early detection of CH. This work scrutinizes the scope of an automatic diagnosis-support system for early detection of CH, by using as indicator the error rate provided by a statistical classifier designed to identify the eye (left vs. right) from iris pixels in color images. Systematic tests were performed on a database with images of 11 subjects (four with CH, four with other ophthalmic diseases affecting the iris pigmentation, and three control subjects). Several aspects were addressed to design the classifier, including: (a) the most convenient color space for the statistical classifier; (b) whether the use of features associated to several color spaces is convenient; (c) the robustness of the classifier to iris spatial subregions; (d) the contribution of the pixels neighborhood. Our results showed that a reduced value for the error rate (lower than 0.25) can be used as CH marker, whereas structural regions of the iris image need to be taken into account. The iris color feature analysis using statistical classification is a potentially useful technique to investigate disorders affecting the autonomous nervous system in CH.

Hide text depending on the three channels of pixels in color images using the modified LSB algorithm

At the moment, with the great development of information and communications technology, the transfer of confidential and sensitive data through public communications such as the Internet is very difficult to keep them from hackers and attackers. Therefore, it is necessary to work on the development of new and innovative ways to transfer such information and protect it to ensure that it reaches the desired goal. The goal of a new technique to hide information design not only hides the secret message behind the center cover, but it also provides increased security. The most common way to transfer important and confidential data is through embedding it into cover medium files in a way that does not affect the accuracy of the carrier file, which is known as hiding. In this paper, encryption and concealment techniques were used to protect data transferred from attackers. The proposed method relied on encryption of confidential information using the encryption key and the Xnor gate, after which the encrypted information was hidden in a color image using the LSB algorithm. The method of concealment depends on the extraction of chromatic channels of three RGB for each pixel and specifying the channel in which the bit of the encryption message will be hidden. Some metrics have been adopted to measure the quality of the resulting picture after hiding as PSNR and MSE, and achieve good results.

A Color Image Encryption Algorithm Based on One Time Key, Chaos Theory, and Concept of Rotor Machine

An innovative method proposed for encrypting color images is comprised of one-time keys and chaos theory using a distinctive concept of rotor machine. The novelty of this scheme is that the rows and columns of 2-dimensional images are converted into circular object called rotor and can be rotated at 360 degrees in clockwise or anti clockwise direction. The rotation will change the existing rotor into new one and can be used in substitution process of plain image. This process can be repeated $\beta $ times and each time a new rotor is created just by a simple rotation. The rotation is performed in terms of pixels so degree of angle is converted into number of pixels. Using this method, same object with new face (after rotation) is used for encryption. The pixels of color image are permuted using the sorted index of logistic sequence. Then, three pseudo-random images are created from Piecewise Linear Chaotic Map (PWLCM). For substitution, both the permuted color channels and pseudo-random images are transformed into rotors. The angle is obtained from Chen chaotic system. The one-time keys are for chaotic maps are generated by using 512-bits hash of plain image. The simulated outcomes demonstrate that the proposed system has high quality of results and requires only single round of encryption to achieve high encryption along with high robustness against the transmission noises.