Aiming at the problem of the failure of the global positioning system in the satellite denied environment, and facing the needs of the navigation and positioning in indoor space, the technology of indoor magnetic field vector positioning and navigation have been studied. Based on the geomagnetic matching technology, the wavelet analysis method is used to estimate the noise components and statistical characteristics of the magnetic field three-dimensional signal; combined with the Kalman filtering algorithm, through estimating and optimizing the magnetic field vector signal, an indoor magnetic positioning signal processing algorithm with wavelet analysis and Kalman fusion is established, realizing the optimization of the measurement accuracy of the geomagnetic signal and the accuracy of the fingerprint map. The experimental results indicate that the signal-to-noise ratio of the test sequenceis increasedby 3db, the rootmean square error is reducedby about30%, and themaximumpositioning error reaches 1.34m, which can meet the requirements of indoor positioning.

Photon-counting detector based spectral computed tomography (CT) can obtain energy-discriminative attenuation map of an object in different energy channels, extending the conventional volumetric image along a spectral dimension. However, compared with the full spectrum data, the noise in a narrower energy channel is significantly increased. In order to improve image quality of spectral CT images, this paper proposes an iterative reconstruction algorithm based on the prior image constrained compressed sensing (PICCS) and dictionary learning (DL) theories, which is called PICCS-DL. The PICCS-DL utilizes the correlation of the images reconstructed from different energy channels by taking the broad spectrum image as a prior constraint, and it utilizes the sparse of the images by taking the total variation (TV) and DL as prior constraints. The alternating minimization, Split-Bregman and the steepest descent (SD) methods are used to solve the objective function. The effectiveness of the proposed method is validated with numerical simulations and preclinical applications. The results demonstrate that the proposed algorithm generally produces superior image quality, especially for noisy and sparse projection data.

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.

Multi-focal image and multi-modal image fusion technology can fully take advantage of different sensors or different times, retaining the image feature information and improving the image quality. A multi-source image fusion algorithm based on bidimensional empirical mode decomposition (BEMD) and a region sharpness-guided region overlapping algorithm are studied in this article. Firstly, source images are decomposed into multi-layer bidimensional intrinsic mode functions (BIMFs) and residuals from high-frequency layer to low-frequency layer by BEMD. Gaussian bidimensional intrinsic mode functions (GBIMFs) are obtained by applying Gaussian filtering operated on BIMF and calculating the sharpness value of segmented regions using an improved weighted operator based on the Tenengrad function, which is the key to comparison selection and fusion. Then, the GBIMFs and residuals selected by sharpness comparison strategy are fused by the region overlapping method, and the stacked layers are weighted to construct the final fusion image. Finally, based on qualitative evaluation and quantitative evaluation indicators, the proposed algorithm is compared with six typical image fusion algorithms. The comparison results show that the proposed algorithm can effectively capture the feature information of images in different states and reduce the redundant information.

Reconstruction of computed tomography (CT) images under sparse data conditions often leads to compromised quality, which can result in diagnostic inaccuracies. This study introduces a novel iterative reconstruction algorithm that combines a second-order differential Laplacian operator with a bilateral weighted relative total variation model to enhance the CT image quality from sparse datasets. The approach is designed to efficiently capture sharp edges and fine textures while reducing noise and maintaining critical edge features. Numerical simulations and preliminary clinical testing demonstrate that the algorithm significantly reduces streak artifacts and improves edge clarity, outperforming traditional methods in both qualitative and quantitative analyses. In summary, the developed iterative reconstruction algorithm substantially enhances the reconstruction quality of CT images with sparse data, showing significant advantages over conventional techniques, and promises to significantly improve the accuracy and reliability of clinical imaging diagnostics.