This article uses the nonlinear digital chaos theory algorithm to generate the corresponding encryption system initial parameters, by analysing the correlation degree of image elements from the angles of horizontal, v...
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This article uses the nonlinear digital chaos theory algorithm to generate the corresponding encryption system initial parameters, by analysing the correlation degree of image elements from the angles of horizontal, vertical, and diagonal direction, in order to study computer three-dimensional (3D) image encryption processing. The correlation degree of the cypher text obtained by the nonlinear algorithm is weak in the image's adjacent pixels, and the adjacent pixels are not related at all, horizontal angle: 0.915989, vertical angle: 0.968184, diagonal angle: 0.913533. The nonlinear algorithm distributes the image's statistical features into the random cypher text. By applying permutations and replacements in 3D space, the proposed approach improves performance parameters and widens key space in comparison to previous image cryptography investigations. The important qualities of such a secure system are its simplicity and efficacy. Simulations and analysis show that the proposed method can produce a large key space while also surviving standard cipher attacks. Because of its powerful cryptographic properties, it is suited for image applications. The nonlinear algorithm has very high sensitivity to the secret key and plaintext, as well as better statistical performance, higher security, and higher efficiency in the operation of the algorithm.
We demonstrated an image denoise method for multiphoton microscopy. The method nonlinearly adjusts pixel brightness according to average brightness comparison between the image global and kernels with selected size. T...
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In this manuscript, we investigate the dynamics of Memristor Cellular nonlinear Networks, focusing on the complex behaviors of 2-terminal locally active volatile threshold switches, known as volatile memristors, using...
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This unique text/reference presents a fresh look at nonlinearprocessing through nonlinear eigenvalue analysis, highlighting how one-homogeneous convex functionals can induce nonlinear operators that can be analyzed w...
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ISBN:
(数字)9783319758473
ISBN:
(纸本)9783319758466
This unique text/reference presents a fresh look at nonlinearprocessing through nonlinear eigenvalue analysis, highlighting how one-homogeneous convex functionals can induce nonlinear operators that can be analyzed within an eigenvalue framework. The text opens with an introduction to the mathematical background, together with a summary of classical variational algorithms for vision. This is followed by a focus on the foundations and applications of the new multi-scale representation based on non-linear eigenproblems. The book then concludes with a discussion of new numerical techniques for finding nonlinear eigenfunctions, and promising research directions beyond the convex case. Topics and features: introduces the classical Fourier transform and its associated operator and energy, and asks how these concepts can be generalized in the nonlinear case; reviews the basic mathematical notion, briefly outlining the use of variational and flow-based methods to solve image-processing and computer vision algorithms; describes the properties of the total variation (TV) functional, and how the concept of nonlinear eigenfunctions relate to convex functionals; provides a spectral framework for one-homogeneous functionals, and applies this framework for denoising, texture processing and image fusion; proposes novel ways to solve the nonlinear eigenvalue problem using special flows that converge to eigenfunctions; examines graph-based and nonlocal methods, for which a TV eigenvalue analysis gives rise to strong segmentation, clustering and classification algorithms; presents an approach to generalizing the nonlinear spectral concept beyond the convex case, based on pixel decay analysis; discusses relations to other branches of imageprocessing, such as wavelets and dictionary based methods. This original work offers fascinating new insights into established signal processing techniques, integrating deep mathematical concepts from a range of different fields, which will be of gr
This study rigorously investigates the effectiveness of nonlinear filters in CMOS for 2-D signal processing to enhance image quality. We comprehensively compare traditional linear filters' performance, which opera...
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This study rigorously investigates the effectiveness of nonlinear filters in CMOS for 2-D signal processing to enhance image quality. We comprehensively compare traditional linear filters' performance, which operate on the principle of linearity, with nonlinear filters, such as the median-median (Med-Med) approach, designed to handle nonlinear data. To ensure the validity of our findings, we use widely accepted metrics like normalized squared error (NSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) to quantify the differences. Our simulations and experiments, conducted under controlled conditions, demonstrate that nonlinear filters in CMOS outperform linear filters in removing impulse noise and enhancing images. We also address the challenges of implementing these algorithms at the hardware level, focusing on power consumption and chip area optimization. Additionally, we propose a new architecture for the Med-Med filter and validate its functionality through experiments using a 9-pixel image sensor array. Our findings highlight the potential of nonlinear filters in CMOS for real-time image quality enhancement and their applicability in various real-world imaging applications. This research contributes to visual technology by combining theoretical insights with practical implementations, paving the way for more efficient and adaptable imaging systems.
In this manuscript, we investigate the dynamics of Memristor Cellular nonlinear Networks, focusing on the complex behaviors of 2-terminal locally active volatile threshold switches, known as volatile memristors, using...
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ISBN:
(数字)9798350351927
ISBN:
(纸本)9798350351934
In this manuscript, we investigate the dynamics of Memristor Cellular nonlinear Networks, focusing on the complex behaviors of 2-terminal locally active volatile threshold switches, known as volatile memristors, using a circuit-theoretic approach. We show that a cell within the array equipped with an NbO
2
-based volatile threshold switch can exhibit both oscillatory and static dynamics depending on the parameters of the Memristor Cellular nonlinear Network. We utilize the latter to design an M-CNN for imageprocessing that performs edge detection on binary input images.
In this work, an exhaustive study on post effect processing of three-dimensional (3D) image is carried to solve the problem of nonlinear digital watermarking algorithm. First, through the feature space decomposition m...
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In this work, an exhaustive study on post effect processing of three-dimensional (3D) image is carried to solve the problem of nonlinear digital watermarking algorithm. First, through the feature space decomposition method of the host image, the embedded watermark is constructed with the full row or column rank of the matrix, and then the public key is constructed by using the existence of the unitary matrix of the full row rank and column rank matrix, so that the algorithm can embed and extract the watermark in an asymmetric way. Watermark extraction correlation coefficient (?) value is 1. When the deformation amplitude of the model is slight and the noise intensity is s = 0.0001, the watermark can be extracted successfully, and the watermark extraction correlation coefficient (?) is 0.92. In addition, the security of the algorithm is analyzed from many angles, the theoretical analysis is given, and verified by the experimental results. The proposed 3D watermarking methods are used to examine the information capacity of various 3D meshes. The 3D watermarking methods' resistance to noise perturbation and object cropping is also investigated.
In this paper, we propose a novel approach to solve nonlinear stress analysis problems in shell structures using an imageprocessing technique. In general, such problems in design optimisation or virtual reality appli...
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In this paper, we propose a novel approach to solve nonlinear stress analysis problems in shell structures using an imageprocessing technique. In general, such problems in design optimisation or virtual reality applications must be solved repetitively in a short period using direct methods such as nonlinear finite element analysis. Hence, obtaining solutions in real-time using direct methods can quickly become computationally overwhelming. The proposed method in this paper is unique in that it converts the mechanical behaviour of shell structures into images that are then used to train a machine learning algorithm. This is achieved by mapping shell deformations and stresses to a set of images that are used to train a conditional generative adversarial network. The network can then predict the solution of the problem for a varying range of parameters. The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. Each structure is studied for a varying geometry and a set of material properties. We show that the results of the trained network agree well with the results of the nonlinear finite element analysis. The proposed approach can quickly and accurately predict the mechanical behaviour of the structure using a fraction of the computational cost. All created data and source codes are openly available.
This work applies sparse representations and nonlinear image processing to two inverse imaging problems. The first problem involves image restoration, where the aim is to reconstruct an unknown high-quality image from...
This work applies sparse representations and nonlinear image processing to two inverse imaging problems. The first problem involves image restoration, where the aim is to reconstruct an unknown high-quality image from a low-quality observed image. Sparse representations of images have drawn a considerable amount of interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. The standard sparse representation, however, does not consider the intrinsic geometric structure present in the data, thereby leading to sub-optimal results. Using the concept that a signal is block sparse in a given basis —i.e., the non-zero elements occur in clusters of varying sizes — we present a novel and efficient algorithm for learning a sparse representation of natural images, called graph regularized block sparse dictionary (GRBSD) learning. We apply the proposed method towards two image restoration applications: 1) single-image super-resolution, where we propose a local regression model that uses learned dictionaries from the GRBSD algorithm for super-resolving a low-resolution image without any external training images, and 2) image inpainting, where we use GRBSD algorithm to learn a multiscale dictionary to generate visually plausible pixels to fill missing regions in an image. Experimental results validate the performance of the GRBSD learning algorithm for single-image super-resolution and image inpainting applications. The second problem addressed in this work involves image enhancement for detection and segmentation of objects in images. We exploit the concept that even though data from various imaging modalities have high dimensionality, the data is sufficiently well described using low-dimensional geometrical structures. To facilitate the extraction of objects having such structure, we have developed general structure enhancement methods th
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