Adjacent Pixels Research Articles

Image Encryption Algorithm Using Multi-Level Permutation and Improved Logistic–Chebyshev Coupled Map

To improve the randomness of the Chebyshev chaotic sequences by coupling the Logistic map and the Chebyshev map, a new one-dimensional Logistic–Chebyshev chaotic map (LCCM) is first presented in this paper. Several tests, including the bifurcation diagram, Lyapunov exponents, and information entropy, are employed to analyze the dynamics of the LCCM. The proposed LCCM has better ergodicity and unpredictability than the traditional Chebyshev map. Next, a new image encryption algorithm based on the LCCM and multi-level manipulation is proposed. The LCCM is used to control the pixel permutation, bit-level shuffling, and subsequent pixel diffusion based on the modulo and XOR operation. Extensive experiments, including histogram analysis, information entropy, adjacent pixel correlation, and key sensitivity, show that the image encryption algorithm has high security and can effectively resist malicious attacks.

Security of Encrypted Images in Network Transmission Based on an Improved Chaos Algorithm

With the wide application of 5G networks, many digital images must rely on networks for transmission. Traditional image encryption algorithms can no longer meet modern security requirements, and it is important to protect digital images in network transmission more securely. To address the shortcomings of traditional chaotic algorithms in image encryption, such as the strong randomness of image pixel replacement and time-consuming computations of image pixel iteration, we use a fractional-order Fourier transform to replace the image pixel matrix, a one-dimensional logistic chaos algorithm to reduce the problem of strong randomness of image pixels, and a sine chaos-based idea to optimize the diffusion algorithm to reduce the computational complexity. After encrypting a digital image in simulation experiments, we achieved better results through statistical analysis, adjacent pixel correlation, and resistance to differential attack performance analysis index tests, and verified the protection effect of this algorithm in digital images during network attacks.

A novel lorenz-sine coupling chaotic system and its application on color image encryption

The chaos-based image encryption has been extensively studied for image security owing to its high efficiency. However, both the complexity of the chaotic system and the chaos-based image encryption method must be enhanced. A Lorenz-Sine coupling chaotic system with complex behavior and a novel image encryption scheme is presented in this work. By adding a nonlinear controller containing sinusoidal function to the Lorenz system, we obtain smooth chaotic attractors with a unique appearance. Analysis of the dynamical properties shows that it has a multi-symmetry stranger attractor and a large chaotic range. Based on the proposed chaotic system, a pseudorandom sequence generator is designed to generate keys with high randomness. Furthermore, an image encryption scheme using spiral-rotation and random permutation is presented. Cipher-feedback is employed to increase the avalanche effect. The encryption efficiency and the security analysis are simulated. The result shows that the correlation coefficient between the adjacent pixels of the proposed scheme achieves The NPCR of the proposed scheme is larger than 0.9661 and the UACI falls within the theoretical value of UACI with a significance level of 0.05. Thus, the proposed scheme is more capable against differential attacks. Furthermore, it is more robust against noise attacks and cropping attacks.

An Image Encryption Algorithm Based on Improved Hilbert Curve Scrambling and Dynamic DNA Coding.

As an effective method for image security protection, image encryption is widely used in data hiding and content protection. This paper proposes an image encryption algorithm based on an improved Hilbert curve with DNA coding. Firstly, the discrete wavelet transform (DWT) decomposes the plaintext image by three-level DWT to obtain the high-frequency and low-frequency components. Secondly, different modes of the Hilbert curve are selected to scramble the high-frequency and low-frequency components. Then, the high-frequency and low-frequency components are reconstructed separately using the inverse discrete wavelet transform (IDWT). Then, the bit matrix of the image pixels is scrambled, changing the pixel value while changing the pixel position and weakening the strong correlation between adjacent pixels to a more significant correlation. Finally, combining dynamic DNA coding and ciphertext feedback to diffuse the pixel values improves the encryption effect. The encryption algorithm performs the scrambling and diffusion in alternating transformations of space, frequency, and spatial domains, breaking the limitations of conventional scrambling. The experimental simulation results and security analysis show that the encryption algorithm can effectively resist statistical attacks and differential attacks with good security and robustness.

Synchronization and desynchronization of chaotic models with integer, fractional and distributed-orders and a color image encryption application

For the first time, as we know, the generalization of combination synchronization (GCS) of chaotic dynamical models with integer, fractional and distributed-orders is studied in this paper. In the literature, this type of synchronization is considered as a generalization of numerous other kinds. We state the definition of GCS and it’s scheme using tracking control technique among two drive integer and fractional-order models and one response distributed-order model. A theorem is established and proven to give us the analytical formula for the control functions in order to achieve GCS. Numerical calculations are utilized to support these analytic results. We give an example to check the validity of the control functions to achieve GCS. Using the modified Predictor-Corrector method, we obtained numerical results for our models that are in good agreement with the analytical ones. In this work, also, we introduce both of the fractional-order hyperchaotic strongly coupled (FOHSC) Lorenz model and distributed-order hyperchaotic strongly coupled (DOHSC) Lorenz model. Since there are few articles on chaos desynchronization, we aim to study the chaos desynchronization of FOHSC and DOHSC Lorenz models. The encryption and decryption of color image are presented based on GCS between two drive integer and fractional-order models, respectively and one response distributed-order model. Information entropy, correlation analysis between adjacent pixels and histograms are determined together with the experimental results of color image encryption.

A Multi-Scale Progressive Collaborative Attention Network for Remote Sensing Fusion Classification.

With the development of remote sensing technology, panchromatic images (PANs) and multispectral images (MSs) can be easily obtained. PAN has higher spatial resolution, while MS has more spectral information. So how to use the two kinds of images' characteristics to design a network has become a hot research field. In this article, a multi-scale progressive collaborative attention network (MPCA-Net) is proposed for PAN and MS's fusion classification. Compared to the traditional multi-scale convolution operations, we adopt an adaptive dilation rate selection strategy (ADR-SS) to adaptively select the dilation rate to deal with the problem of category area's excessive scale differences. For the traditional pixel-by-pixel sliding window sampling strategy, the patches which are generated by adjacent pixels but belonging to different categories contain a considerable overlap of information. So we change original sampling strategy and propose a center pixel migration (CPM) strategy. It migrates the center pixel to the most similar position of the neighborhood information for classification, which reduces network confusion and increases its stability. Moreover, due to the different spatial and spectral characteristics of PAN and MS, the same network structure for the two branches ignores their respective advantages. For a certain branch, as the network deepens, characteristic has different representations in different stages, so using the same module in multiple feature extraction stages is inappropriate. Thus we carefully design different modules for each feature extraction stage of the two branches. Between the two branches, because the strong mapping methods of directly cascading their features are too rough, we design collaborative progressive fusion modules to eliminate the differences. The experimental results verify that our proposed method can achieve competitive performance.

Lightweight digital imaging and communications in medicine image encryption for IoT system

Diagnosis in healthcare systems relies heavily on the use of medical images. Images such as X-rays, ultrasounds, computed tomography (CT) scans, magnetic resonance imaging (MRIs), and other scans of the brain and other internal organs of patients include private and personal information. However, these images are vulnerable to unauthorized users who unlawfully use them for non-diagnostic reasons due to the lack of security in communication routes and the gaps in the storage systems of hospitals or medical centers. Image encryption is a prominent technique used to protect medical images from unauthorized access in addition to enhancing the security of communication networks. In this paper, researchers offer a lightweight cryptosystem for the secure encryption of medical images that makes use of the present block cipher and a five-dimensional chaotic map. More than 25 images from the open science framework (OSF) public database of patients with coronavirus disease 2019 (COVID-19) were used to evaluate the proposed system. DICOM stands for “digital imaging and communications in medicine”. The efficiency of the proposed system is proved in terms of adjacent pixels’ correlation analysis, National Institute of Standards and Technology (NIST) analysis, mean square error, information entropy, unified average changing intensity, peak-to-signal noise ratio, entropy, and structure similarity index image.

Design of a Dual-Mode Multispectral Filter Array.

Multispectral imaging is valuable in many vision-related fields as it provides an additional modality to observe the world. Cameras equipped with multispectral filter arrays (MSFAs) are typically impractical for everyday use due to their intractable demosaicking and chromatic reproduction processes, which restrict their applicability beyond academic research. In this work, a novel MSFA design is proposed to enable dual-mode imaging for multispectral cameras. In addition to a conventional multispectral image, the camera is also able to produce a Bayer-formed RGB image from a single shot by grouping and merging adjacent pixels in the proposed MSFA, making it suitable for scenarios where display-ready RGB images are required. Furthermore, a two-stage optimization scheme is implemented to jointly optimize objective functions for both imaging modes. The evaluation results on multiple datasets suggest that the proposed MSFA design is able to simultaneously achieve competitive spectral reconstruction accuracy compared to elaborate multispectral cameras and chromatic accuracy compared to commercial RGB cameras.

Asynchronous Updating Boolean Network Encryption Algorithm

An asynchronous updating Boolean network is employed to simulate and analyze the gene expression of a particular tissue or species, revealing the life activity process from a system perspective to reveal the disease mechanism and treat the disease. Therefore, to ensure the safe transmission of the asynchronous updating Boolean network in the network, we designed an asynchronous updating Boolean network encryption algorithm based on chaos (ABNEA). First, a novel 2D chaotic system (2D-FPSM) is designed. This system has better performance than the classical 2D chaotic system. It is very suitable for cryptographic systems to generate key streams. Second, an encoding rule is designed to convert the asynchronous updating Boolean network to a Boolean matrix and propagate it on the network as an image. The receiver and sender jointly save the encoding rule. Last, to protect the safe propagation of the Boolean network matrix on the network, the method of synchronous scrambling-diffusion is adapted to encrypt the Boolean network matrix based on the 2D-FPSM. Simulation experiments and security analysis show that the average correlation of adjacent pixels of ciphertext are 0.0010, -0.0010, -0.0020, and the average information entropy is 7.9984. The ABNEA can complete the encryption tasks of asynchronously updating Boolean networks and exhibits good security characteristics.

Magnesium Ingot Stacking Segmentation Algorithm for Industrial Robot Based on the Correction of Image Overexposure Area.

This paper proposes an adaptive threshold segmentation algorithm for the magnesium ingot stack based on image overexposure area correction (ATSIOAC), which solves the problem of mirror reflection on the surface of magnesium alloy ingots caused by external ambient light and auxiliary light sources. Firstly, considering the brightness and chromaticity information of the mapped image, we divide the exposure probability threshold into weak exposure and strong exposure regions. Secondly, the saturation difference between the magnesium ingot region and the background region is used to obtain a mask for the magnesium ingot region to eliminate interference from the image background. Then, the RGB average of adjacent pixels in the overexposed area is used as a reference to correct the colors of the strongly exposed and weakly exposed areas, respectively. Furthermore, in order to smoothly fuse the two corrected images, pixel weighted average (WA) is applied. Finally, the magnesium ingot sorting experimental device was constructed and the corrected top surface image of the ingot pile was segmented through ATSIOAC. The experimental results show that the overexposed area detection and correction algorithm proposed in this paper can effectively correct the color information in the overexposed area, and when segmenting ingot images, complete segmentation results of the top surface of the ingot pile can be obtained, effectively improving the accuracy of magnesium alloy ingot segmentation. The segmentation algorithm achieves a segmentation accuracy of 94.38%.

Uncovering the hidden: Leveraging sub-pixel spectral diversity to estimate plant diversity from space

Remotely sensed spectral diversity has emerged as a promising proxy for plant diversity. However, spectral diversity approaches relate image spectra to plant community diversity by only incorporating variation among adjacent pixels, considering each pixel as a homogeneous entity composed of one class, and disregarding the within-pixel variability. Although such approaches might work for remotely-sensed data with fine spatial resolution, they might not be viable solutions to estimate plant diversity using coarse-resolution data from forthcoming spaceborne imagers. To address the limitations associated with spectral diversity approaches, we proposed a novel approach, known as endmember diversity, for remote estimation of plant diversity through quantifying spectral diversity at the sub-pixel level and taking into account the within-pixel variability. The approach consisted of deriving the number and abundance of distinct spectral entities within each pixel via spectral unmixing. In doing so, we considered the spectral signature of each pixel as a mixture of distinct spectral entities, commonly known as endmembers. We then used the per-pixel endmembers and their abundance to calculate different spectral diversity metrics for every pixel. We assessed the performance of the endmember diversity approach at estimating plant taxonomic and phylogenetic diversity based on two experiments using a simulated spectral dataset and a real-world spaceborne DESIS (DLR Earth Sensing Imaging Spectrometer) dataset. In both experiments, we found significant associations between endmember diversity and in situ plant diversity. Additionally, our method applied to DESIS data outperformed a conventional spectral diversity metric based on the coefficient of variation when applied to 1-m airborne imaging spectroscopy data. Collectively, our results demonstrate the capability of forthcoming spaceborne imagers to monitor local plant diversity.

Crosstalk suppression in CMOS terahertz detectors by using a mushroom-like AMC structure.

The suppression of the crosstalk in a CMOS THz detector is essential for enhancing the performance of detector arrays; however, it presents several technical challenges at the chip level. In this paper, a novel structure featuring a mushroom-like artificial magnetic conductor (M-AMC) is developed to suppress the crosstalk between CMOS THz detectors with on-chip antennas. Three-dimensional simulation results show that the M-AMC structure, which is designed by metal Al and doped-Si materials in the CMOS process, not only reduces the transmission coefficient of the electromagnetic wave between adjacent pixels but also enhances the electric field of the target pixels. A 0.65 THz detector array with a M-AMC structure based on the on-chip antenna was fabricated. Experimental results present that after implanting the M-AMC structure, the noise equivalent power (NEP) at the central frequency of pixels significantly decreases by 315.5%. Moreover, the distribution of NEP becomes more uniform, as evidenced by a reduction in the standard deviation coefficient of 26.3%. This demonstrates the effectiveness of the method in suppressing crosstalk and improving the responsivity of CMOS THz detectors, which can be used for high-performance THz detector arrays.

A rotation-invariant corner detector based on the median of subpixelized triangle

Corners are stable local image features that have played a crucial role in various computer vision and image processing tasks. Existing corner detectors generally assume that the distance between every two adjacent pixels is a constant during curvature estimation. However, this assumption is actually invalid, and thus those pixel-based measures of discrete curvature developed and exploited in the existing corner detectors may suffer instability under rotation transformations. To address this fundamental problem, a novel curvature measure is proposed in this paper, which exploits the median of a subpixelized triangle located at the current point to estimate its discrete curvature. It is shown that our proposed curvature measure is invariant under rotation transformations. Based on this novel curvature measure, a new corner detector is further developed. Extensive experimental results show that our proposed corner detector can deliver superior performance over the existing state-of-the-art methods.

Designing a 1D extended logistic map for a secure image encryption

The performance of the keystream is an important index for determining the security of a cryptosystem. However, the parameter range of the classical one-dimensional chaotic map is limited, and the distribution of the generated chaotic sequence is aggregated. Using the keystream generated by such chaotic systems can lead to a small secret key space and low security of the cryptosystem. To address this, this paper proposes a 1D Extended Logistic Map (1D-ELM), which combines the Sin and Logistic maps. The 1D-ELM has the same control parameters as the Logistic map, but the parameter range in which 1D-ELM exhibits chaotic behavior is larger than that of the Logistic map. The performance of 1D-ELM is evaluated using the Lyapunov exponent, bifurcation diagram, and 0-1 test. Additionally, 1D-ELM is applied in image encryption. In the secret key generation stage, the designed cryptosystem employs a combination of public and private keys, ensuring the algorithm’s security. An attacker needs to find two secret keys to crack the algorithm. Furthermore, in the diffusion stage, most ciphertext values are related to the three adjacent ciphertext values, reducing the correlation of adjacent ciphertext pixels. The experiment verifies that 1D-ELM has good application in image encryption and plays an important role. The designed cryptographic system has high security.

Multiscale residual gradient attention for face anti-spoofing

In the field of face anti-spoofing (FAS), how to extract the representative features to distinguish between real and spoof faces and train the corresponding deep networks are two vital issues. In this paper, we propose a simple but effective end-to-end FAS model based on an innovative texture extractor and a depth auxiliary supervision mechanism. In the feature extraction stage, we first design the residual gradient convolutions based on the redesigned gradient operators, which are used to extract fine-grained texture features. The extraction of texture features is based on multiple scales by dividing the texture differences between living and spoofing faces into three levels reasonably. Then we construct a multiscale residual gradient attention (MRGA) to obtain representative texture features from multiple levels texture features. By combining the proposed feature extractor MRGA and existing vision transformer (ViT), the MRGA-ViT is proposed to generate related semantics and obtain final classification results. In the training stage, we also propose a local depth auxiliary supervision based on a novel adjacent depth loss, which utilizes the correlation information of adjacent pixels adequately compared with traditional depth loss. The proposed MRGA-ViT model achieves competitive performance in generalization and stability ability, e.g., the ACER(%) values of intra testing on OULU-NPU database are 1.8, 2.6, 1.6 ± 1.2 and 1.9 ± 2.7 respectively, the AUC(%) of cross type testing attains 99.45 ± 0.57, the ACER(%) values of cross dataset testing are 28.1 and 36.7 respectively. Experimental results prove that the proposed model is competitive to other state-of-the-art works on generalization and stability performance.

HCformer: Hybrid CNN-Transformer for LDCT Image Denoising.

Low-dose computed tomography (LDCT) is an effective way to reduce radiation exposure for patients. However, it will increase the noise of reconstructed CT images and affect the precision of clinical diagnosis. The majority of the current deep learning-based denoising methods are built on convolutional neural networks (CNNs), which concentrate on local information and have little capacity for multiple structures modeling. Transformer structures are capable of computing each pixel's response on a global scale, but their extensive computation requirements prevent them from being widely used in medical image processing. To reduce the impact of LDCT scans on patients, this paper aims to develop an image post-processing method by combining CNN and Transformer structures. This method can obtain a high-quality images from LDCT. A hybrid CNN-Transformer (HCformer) codec network model is proposed for LDCT image denoising. A neighborhood feature enhancement (NEF) module is designed to introduce the local information into the Transformer's operation, and the representation of adjacent pixel information in the LDCT image denoising task is increased. The shifting window method is utilized to lower the computational complexity of the network model and overcome the problems that come with computing the MSA (Multi-head self-attention) process in a fixed window. Meanwhile, W/SW-MSA (Windows/Shifted window Multi-head self-attention) is alternately used in two layers of the Transformer to gain the information interaction between various Transformer layers. This approach can successfully decrease the Transformer's overall computational cost. The AAPM 2016 LDCT grand challenge dataset is employed for ablation and comparison experiments to demonstrate the viability of the proposed LDCT denoising method. Per the experimental findings, HCformer can increase the image quality metrics SSIM, HuRMSE and FSIM from 0.8017, 34.1898, and 0.6885 to 0.8507, 17.7213, and 0.7247, respectively. Additionally, the proposed HCformer algorithm will preserves image details while it reduces noise. In this paper, an HCformer structure is proposed based on deep learning and evaluated by using the AAPM LDCT dataset. Both the qualitative and quantitative comparison results confirm that the proposed HCformer outperforms other methods. The contribution of each component of the HCformer is also confirmed by the ablation experiments. HCformer can combine the advantages of CNN and Transformer, and it has great potential for LDCT image denoising and other tasks.

Multi-vortex hyperchaotic systems based on memristors and their application to image encryption

In this paper, a new memristor-based fourth-order hyperchaotic circuit that can generate multiple vortex volumes is constructed with the aid of a cosine memristor proposed by scholars. Thanks to the properties of cosine memristors, we find new hyperchaotic systems with full even symmetry based on parameters. The new system can produce multi-vortex properties with infinite equilibrium points and exhibits extreme multi-stability phenomena with the coexistence of an infinite number of attractors. After further study of phase trajectory diagrams, Lyapunov exponential spectrum, bifurcation diagrams, intermittent chaos and transient phenomena, we find that there are complex state transfer phenomena such as period 1 windows, chaos, multiperiodic bifurcations, multi-periodic windows and hyperchaotics, which are constantly alternating. In terms of circuit implementation, Multisim was used to build the circuit and to realise the characteristics of the memristor and the simulation of the hyperchaotic circuit. Finally, a DNA encryption algorithm is designed based on memristor hyperchaotic system, with emphasis on adjacent pixel correlation, encryption histogram, key sensitivity, anti-cropping and anti-noise performance. The results show that the new system has a very large key space, strong key sensitivity, outstanding anti-cropping, and anti-noise performance. The proposed hyperchaotic system thus has high security performance when applied to image encryption.

Image encryption algorithm based on multi-bit superposition and optical chaos

Aiming at the problem that the leakage of single-layer key encryption may lead to the theft of image information, we propose an image encryption algorithm based on multi-bit superposition and optical chaos. The generated optical chaotic sequence is used to encrypt the encrypted image together with the user input key. In the scrambling stage, the 3D-cat map is used to encrypt the bit plane, and the multi-bit superposition method is used to encrypt the key space between the bit planes again by using the sub-block internal and external encryption methods. In the diffusion stage, the Logistic-tent map is used to diffuse the scrambling image to generate the ciphertext image, and the optical chaotic waveform is used as the carrier to mask the ciphertext image information in the transmission process. At the recipient, the ciphertext image information is obtained through the synchronized optical chaotic waveform, and the image is decrypted by using the synchronized optical chaotic sequence and the key transmitted through the secure channel. If only one of the two cannot decrypt the image, the security of image transmission is improved. The simulation results show that the correlation of adjacent pixels of the image encrypted by the algorithm is close to zero, the gray histogram is evenly distributed, the pixel change rate, the normalized average change intensity and the block average change intensity are close to the ideal value, and the sensitivity of the plaintext is further enhanced, which can effectively resist statistical attacks, differential attacks and exhaustive attacks.

Spatial-Aware Multi-Directional Autoencoder For Pre-Training

Abstract Vision Transformers for pre-trained models explore semantic context and spatial relationships for images, which heavily depend on how you select image patches. In this paper, we propose a novel Spatial-aware Multi-directional Patches Multi-cycle Autoencoder (SMPMA) for a pre-trained model that brings the following benefits: (1) Spatial-aware Multi-directional (SM) patches are created with multi-directional spatial locations, transforming a whole image autoencoder problem into a short-span image patches autoencoder problem; (2) SM patches admit a self-cycle autoencoder alignment learning for the first stage and a cross-cycle interaction learning for the second stage, which makes patches align and interact for optimization; (3) SM patches enable to explore local object features and correlation distribution of adjacent pixels by taking arbitrary sampled patches as inputs. Experimental results on four downstream tasks show that our model can achieve state-of-the-art performance over the tasks of image generation, image classification and semantic segmentation.

Local Adaptive Image Filtering Based on Recursive Dilation Segmentation.

This paper introduces a simple but effective image filtering method, namely, local adaptive image filtering (LAIF), based on an image segmentation method, i.e., recursive dilation segmentation (RDS). The algorithm is motivated by the observation that for the pixel to be smoothed, only the similar pixels nearby are utilized to obtain the filtering result. Relying on this observation, similar pixels are partitioned by RDS before applying a locally adaptive filter to smooth the image. More specifically, by directly taking the spatial information between adjacent pixels into consideration in a recursive dilation way, RDS is firstly proposed to partition the guided image into several regions, so that the pixels belonging to the same segmentation region share a similar property. Then, guided by the iterative segmented results, the input image can be easily filtered via a local adaptive filtering technique, which smooths each pixel by selectively averaging its local similar pixels. It is worth mentioning that RDS makes full use of multiple integrated information including pixel intensity, hue information, and especially spatial adjacent information, leading to more robust filtering results. In addition, the application of LAIF in the remote sensing field has achieved outstanding results, specifically in areas such as image dehazing, denoising, enhancement, and edge preservation, among others. Experimental results show that the proposed LAIF can be successfully applied to various filtering-based tasks with favorable performance against state-of-the-art methods.