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Pavement crack assessment is an important indicator for evaluating road health. However, due to the dark color of the asphalt pavement and the texture characteristics of the pavement, current asphalt pavement crack detection technology cannot meet the requirements of accuracy and efficiency. In this paper, we propose an end-to-end multi-scale full convolutional neural network to achieve the semantic segmentation of cracks in road images by learning the crack characteristics in the complex fine grain background of asphalt pavement. The method uses DenseNet and deconvolution network framework to achieve pixel-level detection and fuses features learned from different scales of convolutional kernels through a full convolutional network to obtain richer information on multi-scale features, allowing more detailed representation of crack features in high-resolution images. And the back end joins the SVM classifier to achieve crack classification after crack segmentation. Then we create a road test standard data set containing 12 cracks and evaluate it on the data. The experimental results show that the method achieves good segmentation effect for 12 types of cracks, and the crack segmentation for asphalt pavement is better than the most advanced methods.

The sparse view problem of image reconstruction encountered in computed tomography (CT) is an important research issue due to its considerable potential in lowering radiation dose. Among the researches, the total variation (TV) method is especially effective in sparse view CT reconstruction for its good ability to preserve sharp edges and suppress noise. However, TV-based methods often produce undesired staircase artifacts in smooth regions of the reconstructed images since the reconstructed problem is usually ill-posed and TV regularization favors piecewise constant functions. Moreover, the image can be accurately approximated by sparse coefficients under a proper wavelet tight frame, which has good capability of sparsely estimating the piecewise smooth functions and the quality of reconstructed image can be improved by the sparse prior information. To deal with sparse view CT reconstruction problem, a minimization hybrid reconstruction model that incorporates TV with the wavelet frame has been proposed, which is to use the TV-norm of the low-frequency wavelet frame coefficients and the ℓ0-norm of the high-frequency wavelet frame coefficients to eliminate staircase effect while maintaining sharp edges, simultaneously provide enough regularization in smooth regions. In addition, considering that the two regularization terms produce more parameters, an alternating direction method of multipliers (ADMM) algorithm has been applied to solve the minimization problem by iteratively minimization separately. Finally, compared with several iterative reconstruction methods, the experimental results demonstrate the competitiveness of the proposed method in terms of preserving edges, suppressing staircase artifacts and denoising.

With the fast development of photon counting detection techniques, spectral computed tomography (CT) with a photon counting detector has attracted considerable attention by increasing energy resolution to identify and discriminate materials. The conventional analytic reconstruction algorithms can be directly applied to reconstruct spectral CT images for each spectrum or energy bin. However, a comprehensive evaluation of analytic reconstruction algorithms for spectral CT has not been reported yet. This motivates us to evaluate the analytic spiral cone-beam CT algorithms and to provide a fair comparison platform for the state-of-the-art iterative spectral CT reconstruction algorithms. Considering the fact that a narrow energy bin has high noise which degrades the imaging quality of spectral CT, an adaptive maximum a posterior projection restoration algorithm is first used to reduce the noise, and then, a 2-D/3-D weighted spiral Feldkamp–Davis–Kress algorithm is implemented to reconstruct the spectral CT images at different energy bins. Finally, the principle component analysis is employed to render the spectral reconstruction results into a color space. Our numerical results show that the analytic reconstruction approach is fast and it can provide high spatial resolution, high contrast resolution, and high signal-to-noise-ratio under higher helical pitches. This makes it possible to serve as a platform to evaluate the state-of-the-art iterative spectral CT algorithms.

In deflection tomography, Algebraic reconstruction technique (ART) is a common reconstruction technique. In this paper, we study that projection access order has an important effect on the reconstruction of the measured field in ART. We show that a multilevel scheme (MLS) projection access ordering in ART for deflection tomography. The algorithm is carried out by a three-peak Gaussian simulative temperature field. The simulation results are showed in this paper, and compared with the traditional sequential access projection method. Comparisons show that this algorithm improved reconstruction quality and the speed of convergence, especially in cases with noisy data added.

The variational level set approaches are widely used in image segmentation. To extract the objects from the inhomogeneity image, an improved local and global binary fitting active contours model is presented in this paper. The energy functional has been defined by a local intensity fitting term and a global intensity fitting term. The generalized Gaussian distribution has been used as the kernel function of the local binary fitting information. The experiment results of the homogeneity images, artificial and real inhomogeneous images have shown the advantages of LGBF model in terms of accuracy and robustness.

Energy auto-modulation is an important tool in X-ray imaging, as it can improve the quality and longevity of an x-ray imaging system. Because of the complex nature of imaged objects, X-ray energy auto-modulation may be difficult. If there is a physical model about imaging mechanism, one can forecast the best imaging parameters using a pre-scan that can be fed into this model. This paper offers a physical model, which is called the Voltage-Thickness-Gray (VTG) model. Based on equivalent single-energy, this paper uses the empirical formula of X-ray attenuation and X-ray photon intensity to build this VTG model. Then use linear regression to estimate the model's parameters, by multi-voltage imaging about the steel wedge block. At last, by the experiment of the steel step block, verify this model and forecast the imaging tube voltage. The result shows this model can better reflect X-ray attenuation imaging properties, and can be used to forecast the imaging voltage. Also the forecast precision can achieve 90% or so.

The sparse-view problem of image reconstruction encountered in computed tomography (CT) is an important research issue due to its considerable potential in decreasing radiation dose and improving detection efficiency. Among the current sparse-view CT reconstruction algorithms, iterative reconstruction algorithms that consider total variation (TV) regularization exhibit good performance. However, the gradient difference direction is singular or fixed in conventional TV algorithms, leading to undesired artefacts when minimizing TV in sparse-view CT. To effectively address this issue, based on TV minimization, the gradient difference directional information is introduced as additional prior information in the regularization term, and a new gradient-based directional total variation minimization algorithm is proposed, which adaptively chooses mutative gradient directions to calculate the directional difference operators, and calculates the sum of the direction difference operators. In addition, to solve the fault tolerance and computational load, considering redundant blocks of reconstructed image, we can estimate the gradient directional information of each subblock via the gradient approximation method. For simplicity, the proposed algorithm is termed the BDTV algorithm. To demonstrate the superiority of the proposed algorithm, the simulation data and actual CT data from different algorithms are compared, and the results indicate that the proposed algorithm is effective at preserving details that are lost in the TV minimization, artefact reduction and noise suppression in sparse-view CT.

In conventional X-ray imaging, the ray image of a complicated structural component (complex in shape or structure or composed of multiple materials) often appears to be overexposed or underexposed. This appearance is because of the variations in the effective thickness of the component along the orientation of the X-ray penetration exceed the limit of the dynamic range of the X-ray imaging system. Complete structural information cannot be obtained, and this lack will impact the quality of the X-ray image. To solve this problem, the technology of multi-voltage image stack reconstruction has been advanced. In this new method, the tube voltage is adjusted several times to accommodate the full necessary range of effective thickness, and then, the extracted images are fused to reconstruct the full projection information. The new technology can adequately enlarge the hardware potential to completely represent the internal structure information of a complicated structural component.

A computer code was developed to simulate the operation of radioscopic or tomographic devices. The simulation is based on ray-tracing techniques and on the X-ray attenuation law. However, in the process of projection simulation, we can’t use an expression to describe the distribution of the consecutive X-ray spectrum which is simulated by the Monte Carlo method. We only use numerical integration to realize the projection simulation. So in order to enhance the fidelity of the projection simulation, we research X-ray spectrum sampling technology, and propose projection simulation algorithm based on self-adaptive Simpson integration. The algorithm mainly uses self-adaptive Simpson integration to sampling, based on the distribution of the X-ray spectrum. Then realize the integration of the projection process. With simulation experiment and practical experiment, we have demonstrated that the new sampling method is not only more accurate but also very efficient. This new sampling method is expected to be very useful in X-ray projection simulation, as well as in computing similar sampling or integrals in other area.

In the process of X-ray digital imaging detection, previous test usually adopts the projection data of 360° to reconstruct. But in practical test, it's often restricted to the installation of the equipment and the cost, so it cannot meet the condition of reconstruction. This paper studies a method to solve the coordinate of circle center about a particular component by some experience information, which is based on the projection values of three views in order to determine the distribution of holes. Experiment shows that the method not only reduces the collection angles and the cost, which satisfies the problem of detection in practical application, but also guarantees the accuracy of the positional information of the internal holes, which is compared with the 900 views under reconstruction.

It is quite important to obtain the sensor nodes location information in the underwater acoustic sensor networks localization. A method of acoustic sensor network node self-localization based on adaptive particle swarm optimization is proposed aiming at the stringent difficulties of the underwater acoustic sensor node localization and the shortage of standard particle swarm optimization (PSO) algorithm which is easily trapped into the local optimum. In the method, the global search ability and the local performance of the PSO algorithm are effectively improved by balancing the stochastic inertia weight. At the same time, the proposed method finds easy and elegant solutions to get rid of the local optimization by adopting the adaptive mutation strategy. The experimental results indicated that the new method can effectively solve the current problem in the underwater acoustic sensor node localization, and the pointing accuracy achieves 0.605m.

The convergence speed of algebraic reconstruction technique (ART) depends heavily on the order in which the projections are considered. In this study, a projection access scheme based on prime number increment is proposed, which is applicable to uniform projection sampling in any angle range. We compared the results reconstructed from the proposed method with the results reconstructed from the conventional sequential method, the prime number decomposition method and random ordering method, for cone-beam X-ray computed tomography reconstruction and for the case of circular acquisition. The results indicate that using the proposed method can accelerate the convergence of ART and produces more accurate images with fewer artifacts.

We propose two variable weighted iterative reconstruction algorithms (VW-ART and VW-OS-SART) to improve the algebraic reconstruction technique (ART) and simultaneous algebraic reconstruction technique (SART) and establish their convergence. In the two algorithms, the weighting varies with the geometrical direction of the ray. Experimental results with both numerical simulation and real CT data demonstrate that the VW-ART has a significant improvement in the quality of reconstructed images over ART and OS-SART. Moreover, both VW-ART and VW-OS-SART are more promising in convergence speed than the ART and SART, respectively.