Adjacent Pixel Pairs Research Articles

Application of Image Encryption Algorithm for Wireless Sensor Network in the Security Analysis of Public Big Data

To improve the security of the Wireless Sensor Network (WSN), the image encryption algorithm is studied. Firstly, the shortcomings of current image encryption algorithms are compared. Secondly, based on the chaotic system, the color image encryption algorithm of double chaotic cross-diffusion is built, and the encryption platform is constructed in combination with the current development of intelligent equipment. Finally, the performance of the constructed algorithm and platform is evaluated, and the correlation of adjacent pixel pairs in different color channels and different directions of image encryption is verified. The results indicate that in the designed image encryption platform, the channels of different color are encrypted in different directions, and the encryption effect is good. Compared with the evaluation platform, the practicability of the encryption algorithm is good. After the image is encrypted, the correlation between adjacent pixels is significantly reduced. The image is highly correlated with adjacent pixels in three directions in the first three channels of encryption. After the studied algorithm is encrypted, the correlation coefficient value of adjacent pixel pairs is greatly reduced, and the maximum reduction is 0.97. The information entropy of the red, green, and blue channels before encryption is lower than the information entropy of the three-color channels after encryption. The information entropy of the algorithm is closer to the optimal value of the information entropy. For the index values of Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR), the average UACI of the algorithm is 99.62%, while the average of the UACI of the literature algorithm is 98.33%. The NPCR value of the algorithm is 33.52%, while the NPCR value of the literature algorithm is 31.23%. The encryption and decryption time of the algorithm is shorter than that of the literature algorithm. The constructed algorithm has high efficiency of encryption and decryption. It provides a reference for the enhancement of the security of image data in the WSN.

Two-point FFT-based high capacity image steganography using calendar based message encoding

In this work, we show how to hide a maximum of 8 bits of secret data into the pixel difference value. Adjacent pixel pairs are chosen in a non-sequential manner in the spatial domain without using any seed or key. In spite of having high average embedding capacity and embedding efficiency, our algorithm can withstand visual, structural and statistical attacks. We compare our work with state-of-the-art works and also show that our work is capable of resisting the series of tests provided by the benchmark “StirMark”.

High spatial resolution pixel synthesis structure for full-complex amplitude modulation with twisted nematic LCD.

Various optics applications require a low-cost twisted nematic LCD that can modulate phase and amplitude independently. We propose a high spatial resolution light-synthesis structure that achieves full-complex amplitude modulation with a twisted nematic LCD. The synthesis structure uses two sparse lateral-shift pixel arrays for the x direction and four sparse lateral-shift pixel arrays for the y direction. The structure recreates an image with two superpixels formed from pairs of adjacent pixels in both directions, and as a result, the spatial resolution is only halved in both directions, which yields an appropriate image quality for the synthesis technique. In experiments, we demonstrate the precise control of amplitude and phase using a twisted nematic LCD.

A dual band algorithm for shallow water depth retrieval from high spatial resolution imagery with no ground truth

For shallow water depth retrieval from high spatial resolution satellite images, although numerous empirical models have been developed, it remains impossible to estimate shallow water depths without collection of required ground truth depth. To address this limitation, a new physically based dual band algorithm is developed to estimate shallow water depths using blue and green bands from high spatial resolution multispectral image with no ground truth. The dual band log-linear model is first analytically formulated, which then is used for shallow water depths retrieval by solving all unknown model parameters based on different types of sampling pixels directly extracted from the multispectral image. The adjacent pixel pairs from the intersecting edges of different bottom types across various depths over shallow water area, are employed to calculate the optimal band rotation coefficient unit vector by minimization method. On the basis, the bottom parameter is estimated through the pixels from the coastline. Additionally, the pixels from various depths of same bottom type are also employed to achieve the blue to green band ratio of diffused attenuation coefficient. The sum of the diffuse attenuation coefficients of green band for upwelling and downwelling light is estimated by QAA and Kd algorithms. To evaluate the performance of the proposed algorithm, the GeoEye-1 image covered Jinqing Island and the Chinese Gaofen-2 image across Kaneohe Bay are chosen to achieve shallow water depth by using the proposed algorithm after geo-rectification and atmospheric correction. The validations using the actual water depths show the overall root mean square errors (RMSEs) for the derived water depths are 1.18 m for Jinqing Island and 1.34 m for Kaneohe Bay respectively. Compared to the Lyzenga empirical model, the developed approach can generally achieve slightly better results for shallow water depths with no ground truth data. Finally, the effects of the variation in the model parameters to water depth retrieval are discussed and analyzed.

Contrast Enhancement Based on Layered Difference Representation of 2D Histograms

A novel contrast enhancement algorithm based on the layered difference representation of 2D histograms is proposed in this paper. We attempt to enhance image contrast by amplifying the gray-level differences between adjacent pixels. To this end, we obtain the 2D histogram h(k, k + l ) from an input image, which counts the pairs of adjacent pixels with gray-levels k and k + l , and represent the gray-level differences in a tree-like layered structure. Then, we formulate a constrained optimization problem based on the observation that the gray-level differences, occurring more frequently in the input image, should be more emphasized in the output image. We first solve the optimization problem to derive the transformation function at each layer. We then combine the transformation functions at all layers into the unified transformation function, which is used to map input gray-levels to output gray-levels. Experimental results demonstrate that the proposed algorithm enhances images efficiently in terms of both objective quality and subjective quality.

Local Mutual Information for Dissimilarity-Based Image Segmentation

Connective segmentation based on the definition of a dissimilarity measure on pairs of adjacent pixels is an appealing framework to develop new hierarchical segmentation methods. Usually, the dissimilarity is fully determined by the intensity values of the considered pair of adjacent pixels, so that it is independent of the values of the other image pixels. In this paper, we explore dissimilarity measures depending on the overall image content encapsulated in its local mutual information and show its invariance to information preserving transforms. This is investigated in the framework of the connective segmentation and constrained connectivity paradigms and leads to the concept of dependent connectivities. An efficient probability estimator based on depth functions is proposed to handle multi-dimensional images. Experiments conducted on hyper-spectral and multi-angular remote sensing images highlight the robustness of the proposed approach.

Improved LSB stegananalysis based on analysis of adjacent pixel pairs

We propose a simple, reliable method based on probability of transitions and distribution of adjacent pixel pairs for steganalysis on digital images in spatial domain subjected to Least Significant Bit replacement steganography. Our method is sensitive to the statistics of underlying cover image and is a variant of Sample Pair Method. We use the new method to estimate length of hidden message reliably. The novelty of our method is that it detects from the statistics of the underlying image, which is invariant with embedding, whether the results it calculate are reliable or not. To our knowledge, no steganalytic method so far predicts from the properties of the stego image, whether its results are accurate or not.

Reversible data hiding based on multilevel histogram modification and sequential recovery

This paper proposes a reversible data hiding method for natural images. Due to the similarity of neighbor pixels’ values, most differences between pairs of adjacent pixels are equal or close to zero. In this work, a histogram is constructed based on these difference statistics. In the data embedding stage, a multilevel histogram modification mechanism is employed. As more peak points are used for secret bits modulation, the hiding capacity is enhanced compared with those conventional methods based on one or two level histogram modification. Moreover, as the differences concentricity around zero is improved, the distortions on the host image introduced by secret content embedding is mitigated. In the data extraction and image recovery stage, the embedding level instead of the peak points and zero points is used. Accordingly the affiliated information is much smaller than in those methods of the kind. A sequential recovery strategy is exploited for each pixel is reconstructed with the aid of its previously recovered neighbor. Experimental results and comparisons with other methods demonstrate our method's effectiveness and superior performance.

Single linkage region growing algorithms based on the vector degree of match

The class of the single linkage region growing (SLRG) algorithms, in which pairs of neighboring pixels are compared for merging, is one of the conceptually simplest approaches to image segmentation. A new normalized coefficient, which measures the degree of match between two multivalued vectors, termed the vector degree of match (VDM), provides the SLRG applications with the metric needed to group adjacent pixel pairs. Two new SLRG algorithms, applied to multiband images and exploiting the VDM criterion, are presented. Their major advantage, in comparison with the SLRG implementations found in the literature, is that they required only one user-defined parameter, the VDM Threshold (VDMT), which is a normalized value featuring local adaptivity and having an intuitive physical meaning. The use of an SLRG module is convenient as the first stage of a structured segmentation procedure featuring the following functional properties: (i) it is implemented sequentially; (ii) it combines single linkage, centroid linkage and hybrid linkage criteria; and (iii) its goal is the detection of image areas characterized by low contrast.