Reconstruction Of Gray-scale Images Research Articles

Sub-Nyquist computational ghost imaging with orthonormal spectrum-encoded speckle patterns

Computational ghost imaging generally requires a large number of pattern illumination to obtain a high-quality image. The colored noise speckle pattern was recently proposed to substitute the white noise pattern in a variety of noisy environments and gave a significant signal-to-noise ratio enhancement even with a limited number of patterns. We propose and experimentally demonstrate here an orthonormalization approach based on the colored noise patterns to achieve sub-Nyquist computational ghost imaging. We tested the reconstructed image in quality indicators such as the contrast-to-noise ratio, the mean square error, the peak signal to noise ratio, and the correlation coefficient. The results suggest that our method can provide high-quality images while using a sampling ratio an order lower than the conventional methods.

Conditional Generative Adversarial Networks for 2D core grayscale image reconstruction from pore parameters

Digital cores are of great significance for reservoir structure simulation, oil and gas exploration and development. Most existing digital core reconstruction methods only generate binary cores with complicated implementation processes, among other problems. To address these problems, this study proposed a combination of core pore parameters and conditional generative adversarial network (CGAN) to realize the 2D reconstruction of core grayscale images from only pore parameters (namely, text-to-image synthesis). The current text-to-image synthesis approaches still have many difficulties in generating fine images, but the technologies of image-to-image generation have improved drastically in recent years. Therefore, the proposed method involves two stages to avoid the difficulty of directly generating core grayscale images from pore parameters. In stage I, we preprocessed core sample images to obtain binary-grayscale image pairs, and then used the CGAN to learn the mapping from core binary images to real sample images. At the same time, the pores in the binary images were segmented and extracted to construct the pore component library. In stage II, on the basis of the given pore parameters, the corresponding pores were randomly extracted from the pore component library to generate binary images, and then the generated binary images were used as input for the trained CGAN model to produce core grayscale images. The experimental results showed that the core grayscale images reconstructed by the proposed method meet the pore conditions and reflect the basic characteristics of real cores. • The reconstruction by our method is based on pore parameters rather than images. • The reconstruction is decomposed into two stages: “pore parameters to binary core” and “binary core to grayscale core”. • The method has a general validity and can reconstruct different types of cores according to the research requirements.

Three Dimensional Fractional Fourier-Mellin Transform, and its Applications

The main objective of the paper is to study the three-dimensional fractional Fourier Mellin transforms (3DFRFMT), their basic properties and applicability due to mainly use in the radar system, reconstruction of grayscale images, in the detection of the human face, etc. Only the fractional Fourier transform is based on time-frequency distribution, whereas only the fractional Mellin transform is on scale covariant transformation. Both transforms can discover action in the definite assortment. The fractional Fourier transform is applicable for controlling the range of shift, whereas the fractional Mellin transform is accustomed to managing the range of rotation and scaling of the function. So, combining both transformations, we get an elegant expression for 3DFRFMT, which can be used in several fields. The paper introduces the concept of three-dimensional fractional Fourier Mellin transforms and their applications. Modulation property is the most useful concept in the signal system, radar technology, pattern reorganization, and many more in the integral transform. Parseval's identity applies to the conservation of energy in the universe. Thus we establish the modulation theorem, Parseval's theorem, scaling theorem, analytic theorem for three-dimensional fractional Fourier Mellin transform. We also give some examples of three-dimensional fractional Fourier-Mellin transform on some functions. Finally, we provide three-dimensional fractional Fourier-Mellin transform applications for solving homogeneous and non-homogeneous Mboctara partial differential equations that we can apply with advantages to solve the different types of problems in signal processing systems. The transform is beneficial in a maritime strategy as a co-realtor to control moments in any specific three-dimensional space. The concept is the most powerful tool to deal with any information system problems. After obtaining the generalization, we can explore many more ideas in applying three-dimensional fractional Fourier-Mellin transformations in many real word problems.

Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation.

Nowadays, our lives have benefited from various vision-based applications, such as video surveillance, human identification and aided driving. Unauthorized access to the vision-related data greatly threatens users’ privacy, and many encryption schemes have been proposed to secure images and videos in those conventional scenarios. Neuromorphic vision sensor (NVS) is a brand new kind of bio-inspired sensor that can generate a stream of impulse-like events rather than synchronized image frames, which reduces the sensor’s latency and broadens the applications in surveillance and identification. However, the privacy issue related to NVS remains a significant challenge. For example, some image reconstruction and human identification approaches may expose privacy-related information from NVS events. This work is the first to investigate the privacy of NVS. We firstly analyze the possible security attacks to NVS, including grayscale image reconstruction and privacy-related classification. We then propose a dedicated encryption framework for NVS, which incorporates a 2D chaotic mapping to scramble the positions of events and flip their polarities. In addition, an updating score has been designed for controlling the frequency of execution, which supports efficient encryption on different platforms. Finally, extensive experiments have demonstrated that the proposed encryption framework can effectively protect NVS events against grayscale image reconstruction and human identification, and meanwhile, achieve high efficiency on various platforms including resource-constrained devices.

General image encryption algorithm based on deep learning compressed sensing and compound chaotic system

Many image compression and encryption algorithms based on traditional compressed sensing and chaotic systems are time-consuming, have low reconstruction quality, and are suitable only for grayscale images. In this paper, we propose a general image compression encryption algorithm based on a deep learning compressed sensing and compound chaotic system, which is suitable for grayscale images and RGB format color images. Color images can be directly compressed and encrypted, but grayscale images need copying from 1 channel to 3 channels. First, the original image is divided into multiple 3 × 33 × 33 non-overlapping image blocks and the bilinear interpolation Bilinear and convolutional neural network are used to compress the image, so that the compression network has no restriction on the sampling rate and can obtain high-quality compression of image. Then a composite chaotic system composed of a two-dimensional cloud model and Logistic is used to encrypt and decrypt the compressed image (sliding scrambling and vector decomposition), and finally the decrypted image is reconstructed. In the reconstruction network, the convolutional neural network and bilinear interpolation Bilinear are mainly responsible for reconstructing the contour structure information, and the fully connected layer is mainly responsible for reconstructing and combining the color information to reconstruct a high-quality image. For grayscale images, we also need to calculate the average value of the corresponding positions of the 3 channels of the reconstructed image, and change the 3 channels into 1 channel. The experimental results show that the general image encryption algorithm based on deep learning compressed sensing and compound chaos system has great advantages in data processing quality and computational complexity. Although in the network the color images are used for training, the quality of grayscale image reconstruction is still better than that of other algorithms. The image encryption algorithm has a large enough key space and associates the plaintext hash value with the key, which realizes the encryption effect of one image corresponding to one key, thus being able to effectively resist brute force attacks and selective plaintext attacks. Compared with it in the comparison literature, the correlation coefficient is close to an ideal value, and the information entropy and the clear text sensitivity index are also within a critical range, which enhances the confidentiality of the image.

Modulation and Parsvals Indentity of TwoDimensional Fractional Fourier-MellinTransform

In electronic world an integral transform play an important role due to its properties. Fourier-Mellin transform mainly use in the radar system, reconstruction of gray scale images, in detection of human face etc.In this paper we proved some properties like modulation and Parsvals identity of two dimensional Fourier-Mellin transform.

Physical total energy based objective function model for sparse reconstruction

Image reconstruction from sparse data is one of the key technologies in physical imaging, and it can often be mathematically described as an underdetermined linear inverse problem. Mathematical models for sparse reconstruction often choose the sparseness constraints or data fidelity term directly as objective functions. However, physics concepts and laws for these modeling or solving processes have never been explored. In this paper, sparse reconstruction is investigated for the first time from the perspective of physical motion. Firstly, a physical model is created to describe a particle motion in viscous medium, in which the particle gravity potential energy function is the norm of l2-l1 after the relaxation transformation. In discrete calculations, the particle displacement is determined by the corresponding iterative result, and its velocity can be described as the change between two adjacent iterations. Then, a new mathematical model based on the physical motion model is studied for sparse reconstruction, in which the total energy of particle is chosen as a new objective function and nonnegative displacements as constraints. This new model preserves sparse constrains and fidelity term of original l2-l1 model, and adds the constrains of deviations between two adjacent iterations so as to avoid oscillations caused by large deviations. Furthermore, a targeted gradient projection technique is adopted to solve such a reconstruction model, and its convergence is discussed as well. Especially in this algorithm, the gradient of this new objective function contains the iterative step of previous iteration, and such iterative steps play the role of physical inertia property in iterative process, which can effectively enlarge the iterative steps to accelerate the convergence and avoid local optima. Finally, two sets of experimental results are presented, including natural grayscale image reconstruction and micro focus X-ray defect detection in precision electronic package. The results demonstrate that the proposed method outperforms its competitors distinctly in time efficiency on the basis of guaranteeing the reconstruction quality. Additionally, on detecting internal defects in solder joint of integrated circuit, the proposed method is well performed in retaining edge details of the reconstructed micro focus X-ray images. Therefore, the proposed method can identify the solder joint internal defects more accurately and is more suitable to rapid and precise micro focus X-ray defect detection in industry.

Optimal Contrast Grayscale Visual Cryptography Schemes With Reversing

The visual cryptography scheme (VCS) is an encryption technique that utilizes the human visual system in recovering a secret image and it does not require any complex calculation. However, the contrast of the reconstructed image could be quite low. A number of reversing-based VCSs (or VCSs with reversing) (RVCS) have been proposed for binary secret images, allowing participants to perform a reversing operation on shares (or shadows). This reversing operation can be easily implemented by current copy machines. Some existing traditional VCS schemes without reversing (nRVCS) can be extended to RVCS with the same pixel expansion for binary image, and the RVCS can achieve ideal contrast, significantly higher than that of the corresponding nRVCS. In the application of grayscale VCS, the contrast is much lower than that of the binary cases. Therefore, it is more desirable to improve the contrast in the grayscale image reconstruction. However, when grayscale images are involved, one cannot take advantage of this reversing operation so easily. Many existing grayscale nRVCS cannot be directly extended to RVCS. In this paper, we first give a new grayscale nRVCS with minimum pixel expansion and propose an optimal-contrast grayscale RVCS (GRVCS) by using basis matrices of perfect black nRVCS. Also, we propose an optimal GRVCS even though the basis matrices are not perfectly black. Finally, we design an optimal-contrast GRVCS with a minimum number of shares held by each participant. The proposed schemes can satisfy different user requirements; previous RVCSs for binary images can be viewed as special cases in the schemes proposed here.

The Kosko Subsethood Fuzzy Associative Memory (KS-FAM): Mathematical Background and Applications in Computer Vision

Many well-known fuzzy associative memory (FAM) models can be viewed as (fuzzy) morphological neural networks (MNNs) because they perform an operation of (fuzzy) mathematical morphology at every node, possibly followed by the application of an activation function. The vast majority of these FAMs represent distributive models given by single-layer matrix memories. Although the Kosko subsethood FAM (KS-FAM) can also be classified as a fuzzy morphological associative memory (FMAM), the KS-FAM constitutes a two-layer non-distributive model. In this paper, we prove several theorems concerning the conditions of perfect recall, the absolute storage capacity, and the output patterns produced by the KS-FAM. In addition, we propose a normalization strategy for the training and recall phases of the KS-FAM. We employ this strategy to compare the error correction capabilities of the KS-FAM and other fuzzy and gray-scale associative memories in terms of some experimental results concerning gray-scale image reconstruction. Finally, we apply the KS-FAM to the task of vision-based self-localization in robotics.

Iterative approach for efficient digital terrain model production from CARTOSAT-1 stereo images

This paper proposes a new algorithm for automatic digital terrain model (DTM) generation from high resolution CARTOSAT-1 satellite images. It consists of two major steps: generation of digital surface models (DSM) from stereo scenes and hierarchical image filtering for DTM generation. Automatic georeferencing, dense stereo matching, and interpolation into a regular grid yields a DSM. In the second step, the DSM pixels are classified into ground and nonground regions using an algorithm motivated from gray-scale image reconstruction to suppress unwanted elevated pixels. Nonground regions, i.e., 3D objects as well as outliers are iteratively separated from the ground regions. The generated DTM is qualitatively and quantitatively evaluated. Height profiles and comparisons between the generated DSM, derived DTM, and ground truth data are presented. The evaluation indicates that almost all nonground objects regardless of their size are eliminated and appropriate results are archived in hilly as well as smooth residential areas. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

Sparsely connected autoassociative fuzzy implicative memories and their application for the reconstruction of large gray-scale images

Autoassociative fuzzy implicative memories (AFIMs) are models that exhibit optimal absolute storage capacity and an excellent tolerance with respect to incomplete or eroded patterns. As a consequence, they can be effectively used for the reconstruction of gray-scale images. In practice, however, applications of AFIMs are confined to images of small size due to computational limitations. In order to circumvent this computational overhead and, motivated by the sparsity of biological neural networks, this paper introduces the class of sparsely connected AFIMs (SCAFIMs). Such as the original AFIMs, SCAFIMs exhibit optimal absolute storage capacity and tolerance with respect to incomplete or eroded patterns. By means of computational experiments, we investigate the performance of SCAFIMs with different network topologies and compare the novel models with other techniques for the reconstruction of gray-scale images.

EXACT GRAYSCALE IMAGE RECONSTRUCTION FROM PROJECTIONS

This paper proposes a new method for the exact reconstruction of gray-scale images from projections. The image projections construct an accumulator array, which is used afterwards to reconstruct the original grayscale image by applying the proposed decomposition algorithm. The proposed method determines the number of projections and the number of rays in each projection that are required in order to achieve the reconstruction. These two parameters also define the dimensions of the accumulator array. Using an accumulator array with proper dimensions ensures that there is always a unique characteristic sample for each pixel, which is used during the reconstruction process to extract the pixel's grayscale value. During the reconstruction phase, the sinusoidal contribution of each pixel is removed from the accumulator array. At the end of the decomposition process the accumulator array becomes empty and the original image is exactly reconstructed. The experimental results confirm the robustness and efficiency of the proposed method.

RBF-based neurodynamic nearest neighbor classification in real pattern space

Superposition of radial basis functions centered at given prototype patterns constitutes one of the most suitable energy forms for gradient systems that perform nearest neighbor classification with real-valued static prototypes. It is shown in this paper that a continuous-time dynamical neural network model, employing a radial basis function and a sigmoid multi-layer perceptron sub-networks, is capable of maximizing such an energy form locally, thus performing almost perfectly nearest neighbor classification, when initiated by a distorted pattern. The proposed design scheme allows for explicit representation of prototype patterns as network parameters, as well as augmenting additional or forgetting existing memory patterns. The dynamical classification scheme implemented by the network eliminates all comparisons, which are the vital steps of the conventional nearest neighbor classification process. The performance of the proposed network model is demonstrated on binary and gray-scale image reconstruction applications.