Maximum Entropy Image Research Articles

Maximum entropy image restoration in astronomy

Maximum entropy image restoration in astronomy

Maximum entropy multi-threshold image segmentation based on improved particle swarm optimization

To solve the problem of low speed and low precision of traditional maximum entropy image segmentation algorithm, a multi-threshold image segmentation algorithm based on improved particle swarm optimization algorithm is proposed. Taking the optimal threshold optimization problem in multi-threshold image segmentation as the research object, the optimal objective function is obtained by using the maximum entropy multi-threshold segmentation method, and then the maximum entropy method and particle swarm optimization algorithm are fused. In order to solve the problem that particle swarm optimization (PSO) is prone to fall into local optimization in the later iteration process, the PSO is improved and the expansion model is added. Finally, the maximum entropy multi-threshold image segmentation method based on the standard particle swarm optimization algorithm and the maximum entropy multi-threshold image segmentation method based on the improved particle swarm optimization algorithm segment the h-component image (hue, saturation, value) of HSV. Images are converted from images. The segmentation results of the two algorithms are evaluated by running time, and the structural similarity of the algorithms is evaluated. evaluation result. Experimental results show that the improved maximum entropy multi-threshold image segmentation algorithm based on particle swarm optimization can better achieve complex image segmentation, and the algorithm has stronger real-time performance.

Image Segmentation Method with Maximum Entropy Optimized by Wolf Pack Algorithm

In order to improve the accuracy and efficiency of image segmentation, the wolf pack algorithm is introduced into the maximum entropy image segmentation method. The wolf pack algorithm is applied to the calculation of the maximum entropy threshold, and the image segmentation is realized by finding the optimal threshold. Simulation results show that this method can quickly and accurately find the segmentation threshold, obtain better segmentation accuracy, and improve the image segmentation effect to a certain extent.

Automatic gas leak detection system

With the development of submarine oil and gas resources, people pay more and more attention to the safety of pipeline transportation. Once an oil and gas leak occurs in the submarine transportation pipeline, it will not only cause direct economic loss but also damage the marine ecology. Therefore, it is particularly important for underwater gas leak detection research. Due to the difficult underwater detection operation and the high labor cost, an embedded module with automatic gas leak detection function is designed and completed in this paper. This paper analyzes the imaging characteristics of multi-beam sounding sonar images, and designs a set of underwater gas leak detection algorithms. The algorithm mainly includes: median filtering, bottom detection, top-hat processing, maximum entropy image segmentation, connected area labeling, nearest neighbor interpolation based on backward mapping, linear upward trend estimation based on Hough transform, and testing of 120 images, With an accuracy rate of more than 98%.

Maximum Entropy Image Segmentation Method Based On Improved Firefly Algorithm

An adaptive parametric firefly algorithm is proposed for the premature convergence of the firefly algorithm itself and the late oscillation of the algorithm iteration. In the image threshold segmentation test experiment, the algorithm is used to optimize the maximum entropy image segmentation, and the maximum entropy and the basic firefly improved maximum entropy algorithm are compared. At the same time, two important indicators of image segmentation evaluation index regional consistency are used to evaluate the results. It shows that the experimental results of the algorithm have better intra-regional consistency and noise immunity.

Multi-GPU maximum entropy image synthesis for radio astronomy

The maximum entropy method (MEM) is a well known deconvolution technique in radio-interferometry. This method solves a non-linear optimization problem with an entropy regularization term. Other heuristics such as CLEAN are faster but highly user dependent. Nevertheless, MEM has the following advantages: it is unsupervised, it has a statistical basis, it has a better resolution and better image quality under certain conditions. This work presents a high performance GPU version of non-gridding MEM, which is tested using real and simulated data. We propose a single-GPU and a multi-GPU implementation for single and multi-spectral data, respectively. We also make use of the Peer-to-Peer and Unified Virtual Addressing features of newer GPUs which allows to exploit transparently and efficiently multiple GPUs. Several ALMA data sets are used to demonstrate the effectiveness in imaging and to evaluate GPU performance. The results show that a speedup from 1000 to 5000 times faster than a sequential version can be achieved, depending on data and image size. This allows to reconstruct the HD142527 CO(6-5) short baseline data set in 2.1 minutes, instead of 2.5 days that takes a sequential version on CPU.

The vision guidance and image processing of AGV

Firstly, the principle of AGV vision guidance is introduced and the deviation and deflection angle are measured by image coordinate system. The visual guidance image processing platform is introduced. In view of the fact that the AGV guidance image contains more noise, the image has already been smoothed by a statistical sorting. By using AGV sampling way to obtain image guidance, because the image has the best and different threshold segmentation points. In view of this situation, the method of two-dimensional maximum entropy image segmentation is used to solve the problem. We extract the foreground image in the target band by calculating the contour area method and obtain the centre line with the least square fitting algorithm. With the help of image and physical coordinates, we can obtain the guidance information.

Application of a multiscale maximum entropy image restoration algorithm to HXMT observations**Supported by Strategic Priority Research Program on Space Science, Chinese Academy of Sciences (XDA04010300) and National Natural Science Foundation of China (11403014)

This paper introduces a multiscale maximum entropy (MSME) algorithm for image restoration of the Hard X-ray Modulation Telescope (HXMT), which is a collimated scan X-ray satellite mainly devoted to a sensitive all-sky survey and pointed observations in the 1–250 keV range. The novelty of the MSME method is to use wavelet decomposition and multiresolution support to control noise amplification at different scales. Our work is focused on the application and modification of this method to restore diffuse sources detected by HXMT scanning observations. An improved method, the ensemble multiscale maximum entropy (EMSME) algorithm, is proposed to alleviate the problem of mode mixing exiting in MSME. Simulations have been performed on the detection of the diffuse source Cen A by HXMT in all-sky survey mode. The results show that the MSME method is adapted to the deconvolution task of HXMT for diffuse source detection and the improved method could suppress noise and improve the correlation and signal-to-noise ratio, thus proving itself a better algorithm for image restoration. Through one all-sky survey, HXMT could reach a capacity of detecting a diffuse source with maximum differential flux of 0.5 mCrab.

The Quick Method for Image Registration Combined with Information Entropy and Cross-Correlation Matrix

The current image registration technique based on gray-level information has shortcomings on time and amount of computation brought by the whole image registration, this paper proposes a quick registration method, which makes a combination of image information entropy and cross-correlation matrix: First, segmenting the target image into blocks and calculating to get the maximum entropy image block, then using it as a template to calculate the cross-correlation matrix with floating image; Second, making the point where the maximum cross-correlation value ​​is as the upper left corner, grasping a same-size block with template on floating image; Finally, obtaining registration parameters through calculation for these two blocks to achieve the purpose of registration. Experimental results show that this method has less computational complexity with the similar registration results, and takes less time. It’s effective and feasible.

Aerial Transmission Lines Image Restoration Technology Research Based on the Improved Maximum Entropy

According to the limitations of the shooting environment, captured image exist the phenomenon of image blurring and noise. This paper proposes that the improved maximum entropy method recovery blurred image which acquire in aerial. Finally, according to the first order Markoff theory to evaluate the quality of the processed image, the results show that maximum entropy image restoration method compared to the conventional approach increase image clarity and details more better.

The Application of Particle Swarm Algorithm based on the Global Optimal Position Mutation in Maximum Entropy Image Segmentation

The Application of Particle Swarm Algorithm based on the Global Optimal Position Mutation in Maximum Entropy Image Segmentation

Evaluation of single and multi-thresholdentropy-based algorithms for folded substrate analysis

This paper presents a detailed evaluation of two variants of Maximum Entropy image segmentation algorithm (single and multi-thresholding) with respect to their performance on segmenting test images showing folded substrates. The segmentation quality was determined by evaluating values of four different measures: misclassification error, modified Hausdorff distance, relative foreground area error and positive-negative false detection ratio. New normalization methods were proposed in order to combine all parameters into a unique algorithm evaluation rating. The segmentation algorithms were tested on images obtained by three different digitalisation methods covering four different surface textures. In addition, the methods were also tested on three images presenting a perfect fold. The obtained results showed that Multi-Maximum Entropy algorithm is better suited for the analysis of images showing folded substrates.

Ordering Double Perovskite Hydroxides by Kinetically Controlled Aqueous Hydrolysis

The precipitation of crystals with stoichiometric and ordered arrangements of distinct metal cations often requires carefully designed molecular precursors and/or sufficient activation energy in addition to the necessary mass transport. Here, we study the formation of ordered double perovskite hydroxides, MnSn(OH)(6) and CoSn(OH)(6), of the generic chemical formula, BB'(OH)(6) (no A site), using kinetic control of aqueous hydrolysis from simple metal salt solutions. We find that the precipitation yields ordered compounds only when the B ion is Mn(II) or Co(II), and not when it is any other divalent transition metal ion, or Zn(II). The key step in forming the compounds is the prevention of rapid and uncontrolled hydrolysis of Sn(IV), and this is achieved by a fluoride counteranion. The two compounds, MnSn(OH)(6) and CoSn(OH)(6), are studied by high-resolution synchrotron X-ray diffraction and from the temperature dependence of magnetic behavior. From maximum entropy image restoration of the electron density and from Rietveld analysis, the degree of octahedral distortion and tilting and the small extent of anti-site disorder are determined. From the nonoverlapping electron density, we infer strongly ionic character of bonding. As the first magnetic study of such materials, we report simple paramagnetic behavior with no long-range magnetic order down to 2 K for the Mn(II) compound, while the cobalt compound presents uncompensated antiferromagnetic interactions, attributed to the single-ion anisotropy of octahedral Co(II).

Resolving the obscuring torus in NGC 1068 with the power of infrared interferometry: revealing the inner funnel of dust

We present new interferometric data obtained with MIDI (MID infrared Interferometric instrument) for the Seyfert II galaxy NGC 1068, with an extensive coverage of sixteen uv points. These observations resolve the nuclear mid-infrared emission from NGC 1068 in unprecedented detail with a maximum resolution of 7 mas. For the first time, sufficient uv points have been obtained, allowing us to generate an image of the source using maximum entropy image reconstruction. The features of the image are similar to those obtained by modelling. We find that the mid-infrared emission can be represented by two components, each with a Gaussian brightness distribution. The first, identified as the inner funnel of the obscuring torus, is hot (800K), 1.35 parsec long, and 0.45 parsec thick in FWHM at a PA=-42 degrees (from north to east). It has an absorption profile different than standard interstellar dust and with evidence for clumpiness. The second component is 3 by 4 pc in FWHM with T=300K, and we identify it with the cooler body of the torus. The compact component is tilted by 45 degrees with respect to the radio jet and has similar size and orientation to the observed water maser distribution. We show how the dust distribution relates to other observables within a few parsecs of the core of the galaxy such as the nuclear masers, the radio jet, and the ionization cone. We compare our findings to a similar study of the Circinus galaxy and other relevant studies. Our findings shed new light on the relation between the different parsec-scale components in NGC 1068 and the obscuring torus.

Maximum entropy regularization of time-dependent geomagnetic field models

SUMMARY We incorporate a maximum entropy image reconstruction technique into the process of modelling the time-dependent geomagnetic field at the core‐mantle boundary (CMB). In order to deal with unconstrained small lengthscales in the process of inverting the data, some core field models are regularized using a priori quadratic norms in both space and time. This artificial damping leads to the underestimation of power at large wavenumbers, and to a loss of contrast in the reconstructed picture of the field at the CMB. The entropy norm, recently introduced to regularize magnetic field maps, provides models with better contrast, and involves a minimum of a priori information about the field structure. However, this technique was developed to build only snapshots of the magnetic field. Previously described in the spatial domain, we show here how to implement this technique in the spherical harmonic domain, and we extend it to the time-dependent problem where both spatial and temporal regularizations are required. We apply our method to model the field over the interval 1840‐1990 from a compilation of historical observations. Applying the maximum entropy method in space—for a fit to the data similar to that obtained with a quadratic regularization—effectively reorganizes the magnetic field lines in order to have a map with better contrast. This is associated with a less rapidly decaying spectrum at large wavenumbers. Applying the maximum entropy method in time permits us to model sharper temporal changes, associated with larger spatial gradients in the secular variation, without producing spurious fluctuations on short timescales. This method avoids the smearing back in time of field features that are not constrained by the data. Perspectives concerning future applications of the method are also discussed.

Blind multichannel reconstruction of high-resolution images using wavelet fusion

We developed an approach to the blind multichannel reconstruction of high-resolution images. This approach is based on breaking the image reconstruction problem into three consecutive steps: a blind multichannel restoration, a wavelet-based image fusion, and a maximum entropy image interpolation. The blind restoration step depends on estimating the two-dimensional (2-D) greatest common divisor (GCD) between each observation and a combinational image generated by a weighted averaging process of the available observations. The purpose of generating this combinational image is to get a new image with a higher signal-to-noise ratio and a blurring operator that is a coprime with all the blurring operators of the available observations. The 2-D GCD is then estimated between the new image and each observation, and thus the effect of noise on the estimation process is reduced. The multiple outputs of the restoration step are then applied to the image fusion step, which is based on wavelets. The objective of this step is to integrate the data obtained from each observation into a single image, which is then interpolated to give an enhanced resolution image. A maximum entropy algorithm is derived and used in interpolating the resulting image from the fusion step. Results show that the suggested blind image reconstruction approach succeeds in estimating a high-resolution image from noisy blurred observations in the case of relatively coprime unknown blurring operators. The required computation time of the suggested approach is moderate.

Maximum entropy image deconvolution applied to structure determination for crystal Nd 1.85Ce 0.15CuO 4− δ

Eight [100] images from a through-focus series of tetragonal crystal Nd 1.85Ce 0.15CuO 4− δ were transformed separately into the structure images by means of maximum entropy image deconvolution. The constructed projected structure model based on the deconvoluted image is confirmed by image simulation. It is demonstrated that the image deconvolution is still successful even when some reflections fall in the vicinity of zero cross of contrast transfer function. The effectiveness and advantages of the technique are discussed.

Doppler imaging and surface differential rotation of young open cluster stars - I. HD 307938 (R58) in IC 2602

In this paper we present Doppler images of a young active G dwarf (HD 307938) in the southern open cluster IC 2602. Spectroscopic data were obtained over a four-night period in 2000 January at the 3.9-m Anglo-Australian Telescope using the University College London Echelle Spectrograph. Simultaneous photometric observations (in the V and R bands) were obtained at the 1.0-m Australian National University telescope. By applying least-squares deconvolution (LSD) to the 2500+ photospheric lines in each echelle spectrum a single high signal-to-noise ratio LSD profile was produced for each phase of the spectroscopic observations. Maximum-entropy image reconstruction, incorporating both the LSD profiles and the photometric data, was used to produce maps of the surface features of the star, with the inclusion of the photometric data producing an increase (compared with the use of spectroscopic data alone) in the spot occupancy in both low- and mid-latitude regions of the star. The maps show that HD 307938 possesses a large, broken polar spot extending down to ∼60° latitude, as well as lower-latitude spots similar to other rapidly rotating G dwarfs. By incorporating a solar-like differential rotation law into the imaging process the surface differential rotation of HD 307938 was determined. This gave a surface shear of dΩ= 0.025 ± 0.015 rad d−1 (for an inclination angle of 60°). Thus the equator of HD 307938 laps the poles every ∼250 d and has a photospheric shear around half that of the Sun.

A further discussion on the peculiarity of maximum entropy image deconvolution in HREM

Some important features observed in the maximum entropy image deconvolution in HREM were studied and demonstrated as a supplement to previous publications in Ultramicroscopy 35 (1991) 339 and 62 (1996) 141. It has been shown that in the process of image deconvolution how the errors of assigned electron-optical parameters can be compensated and the effect of crystal thickness can be corrected. The ‘negative-potential problem’ has been solved more perfectly than before via the normalization of sum total entropy.

Maximum Entropy imaging with INTEGRAL/SPI data

The application of the Maximum Entropy method for sky imaging with the SPI instrument on INTEGRAL is de- scribed. While intended primarily for extended emission, point sources are also mapped by this method. The technique is implemented as the program spiskymax in the data analysis distribution provided by the INTEGRAL Science Data Centre. We briefly introduce the method, describe the particular requirements for the application to SPI, and show some example images using flight data from the early mission.