The denoising of InSAR phase images with fringes of great density variety and high noise level is particularly challenging. In this paper, an efficient technique based on variational image decomposition is proposed to remove noise from an InSAR phase image. We propose a new image decomposition model BL-Hilbert-BM3D to decompose an InSAR phase image into three components: fringes of low density, fringes of high density and noise. They are described by Beppo Levi space (BL), Hilbert space and Block Matching and 3D function space (BM3D) respectively. So our model is able to sufficiently smooth fringes of low density as well as perfectly preserve fringes of high density. We test the proposed method on a simulated and an actual InSAR phase images. We compare the results yielded by our method with those by four other widely used and well-known methods in terms of both quantitative evaluation and visual quality. The experimental results have demonstrated the validity of the proposed method.
We propose an image encryption algorithm based on gradient decomposition and an improved logistic map. The original image is first decomposed into three subimages with different gradient sizes using the gradient decomposition. The pixel position of the three subimages is then changed by applying Arnold transformation, and the random sequence generated by the improved logistic map is used to perform the XOR operation on the three subimages. Subsequently, the row pixel values of the three subimages are converted into binary sequences, and then the binary sequences are shuffled with the random sequences generated by the improved logistic map. Finally, the three subimages are obtained as the R, G, and B components of the color image to generate the final encrypted image. For image decryption, the original image can be simply restored by joining the three subimages. The experimental results show that the improved logistic map has a larger key space and better randomness compared with the classical logistic map. Furthermore, the proposed encryption scheme exhibits better robustness against various attacks than several existing encryption algorithms.
In this paper, the method of Radial basis function(RBF) to Electronic Speckle Pattern Interferometry (ESPI)information extraction is studied, mainly including: the filtering method based on radial basis function for ESPI fringe patterns with wide density; introducing the radial basis function to interpolate the number of fringe in the fringe skeleton method. Thermal deformation phase measurement of Al2O3 ceramic substrate at the circumstance of thermal load was estimated based on the ESPI. In the experiment, four ESPI fringe patterns at different moment at the beginning of the experiment were captured. The RBF filtering method and the fringe skeleton method with RBF interpolating were used to estimating the thermal deformation phase measurement. The acquiring out-of-plane displacements by our method were in good agreement with the real deformation under the stepped-up thermal load gradually. This measurement can provide assistance for studying the performance of ceramic substrate in the process of laser processing.
With the development of artificial intelligence technology, intelligent fringe processing is a goal of relevant researchers in optical interferometry. We propose an intelligent method to achieve fully automated extraction of the fringe skeletons in electronic speckle pattern interferometry (ESPI) based on U-Net convolutional neural network. In the proposed method, the network is first trained by the samples that consist of the noisy ESPI fringe patterns and the corresponding skeleton images. After training, the other multiframe ESPI fringe patterns are fed to the trained network simultaneously; the corresponding skeleton images can be obtained in batches. Using our method, it is not necessary to process fringe patterns frame by frame. Our method is especially suitable for multiframe fringe patterns processing. We apply the intelligent method to one computer-simulated and one real-dynamic ESPI measurement, respectively. For the simulated measurement, it takes just 40 s to obtain the skeleton images of 20 noisy ESPI fringe patterns using our method. Even for low-quality experimental obtained ESPI fringe patterns, our method can also give desired results.
Nuclear graphite has been widely used as moderating and reflecting materials. However, due to severe neutron irradiation under high temperature, nuclear graphite is prone to deteriorate, resulting in massive microscopic flaws and even cracks under large stress in the later period of its service life. It is indispensable, therefore, to understand the fracture behavior of nuclear graphite to provide reference to structural integrity and safety analysis of nuclear graphite members in reactors. In this paper, we investigated the fracture expansion in nuclear graphite based on PDE image processing methods. We used the second-order oriented partial differential equations filtering model (SOOPDE) to denoise speckle noise, then used the oriented gradient vector fields for to obtain skeletons. The full-field displacement of fractured nuclear graphite and the location of the crack tip were lastly measured under various loading conditions.
Partial differential equations (PDEs) and ordinary differential equations (ODE) based image processing methods have been demonstrated to be a powerful tool for optical fringe processing. In this paper, we review our works on PDEs (ODEs)-based image processing methods for optical interferometry fringe processing, including the anisotropic filters, the ODE enhancement methods, the ODE- PDEs filtering and enhancing models and the skeletonization of optical interferometry fringe based on PDEs.
KEYWORDS: Image filtering, Fringe analysis, Monte Carlo methods, Speckle pattern, Interferometry, Denoising, Speckle, Electronic filtering, Image processing, Signal to noise ratio
Noise reduction is one of the largest problems and biggest difficulties involved in electronic speckle pattern
interferometry. We present the new filtering method for electronic speckle pattern interferometry fringes (ESPIF) images
based on Markov chain Monte Carlo methods. We test the proposed method on the computer-simulated speckle fringe
patterns and an experimentally obtained fringe pattern, respectively. In all cases, the proposed method gives desired
results. Experimental results have confirmed that the proposed method is capable of removing noise in ESPIF images
effectively and enhancing the contrast of fringe patterns.
Noise reduction is one of the largest problems and biggest difficulties involved in electronic speckle pattern
interferometry (ESPI). Although the second-order PDEs denoising method is a useful tool of noise reduction for the
ESPI fringe patterns, its main drawback is that the second-order PDE model does not remove impulse noise, a 3×3
mean window filter is generally needed to improve the fringes. For overcome this main drawback, in this paper we apply
the fourth-order PDE denoising model to the computer-simulated and experimentally obtained ESPI fringe, respectively.
In both tests, the fourth-order PDE denoising model clearly outperforms the second-order PDE denoising model.
Experimental results have confirmed that the fourth-order PDE denoising model is capable of removing noise in ESPI
fringe images effectively.
KEYWORDS: Image segmentation, Image processing algorithms and systems, Neural networks, Color image segmentation, Data centers, Neurons, Medical imaging, Evolutionary algorithms, Detection and tracking algorithms, RGB color model
This paper describes a new neural network model that performs color image segmentation in an unsupervised manner. The
new scheme is called enhancing learning algorithm on the radial basis function neural network (ERBF). First, ERBF
employs a dynamic nearest neighbor-clustering algorithm to set its front two layers: the input layer and the hidden layer.
Second, ERBF network introduces the Hebb rule to train the hidden layer and divide the hidden neurons center vectors
into two meaningful groups: one group members are the target color-clustering centers; the other group members are the
background color-clustering centers. Finally, the ERBF output layer is trained by the competitive algorithm and outputs
different values with different input values so as to divide the target region from the image. The present model avoids the
trouble in deciding the hidden nodes number beforehand and needs only to be trained twice. This new color image
segmentation scheme has been implemented and tested on medical color images. The results shown that the new
segmentation scheme is proved to be an effective segmentation algorithm.
Based on image and graphic technologies and synthesized preceding research achievements in the digital processing of photoelastic image, a vectorization system for interactive photoelastic image processing has been set up. The system consists of seven modules, and a toolbox. This paper describes the system structure, new techniques used in the system, and its features. By use of this system, the vectorized image files of the skeleton for stress analysis can be obtained. The application area of the system is wide, and it has quite strong interactive processing capability and user-friendly interface.
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