Infrared (IR) small target detection is gradually attracting a cornucopia of attention and developing rapidly in the application of infrared search and track (IRST) systems. However, it is often hard to extract small targets with adequate feasibility and reliability especially under a variety of different complex backgrounds. To improve the detection performance of small target embedded in sophisticated clutters, a novel hollow side window filter (HSWF) is designed to cope with the background estimation problem. Moreover, the saliency map constructed by heterogeneity filter is utilized as a critical weighting factor during the process of HSWF. Finally, with the help of commonly adopted patch contrast measure (PCM), the small target could be easily identified from hadamard product of the corresponding response maps. The effectiveness and outstanding performance of the proposed method are validated through comprehensive experiments and analysis.

The robust detection of small targets is crucial in infrared (IR) search and tracking applications. Considering that many state-of-the-art (SOTA) methods are still unable to suppress various edges satisfactorily, especially under complex backgrounds, an effective infrared small target detection algorithm inspired by modified fast saliency and the weighted guided image filter (WGIF) is presented in this paper. Initially, the fast saliency map modulated by the steering kernel (SK) is calculated. Then, a set of edge-preserving smoothed images are produced by WGIF using different filter radii and regularization parameters. After that, utilizing the fuzzy sets technique, the background image is predicted reasonably according to the results of the saliency map and smoothed or non-smoothed images. Finally, the differential image is calculated by subtracting the predicted image from the original one, and IR small targets are detected through a simple thresholding. Experimental results on four sequences demonstrate that the proposed method can not only suppress background clutter effectively under strong edge interference but also detect targets accurately with a low false alarm rate.

Velocity analysis using the semblance spectrum can provide an effective velocity model for advanced seismic imaging technology, in which the picking accuracy of velocity analysis is significantly affected by the resolution of the semblance spectrum. However, the peak broadening of the conventional semblance spectrum leads to picking uncertainty, and it cannot deal with the amplitude-variation-with-offset (AVO) phenomenon. The well-known AB semblance can process the AVO anomalies, but it has a lower resolution compared with conventional semblance. To improve the resolution of the AB semblance spectrum, we propose a new weighted AB semblance based on principal component analysis (PCA). The principal components or eigenvalues of seismic events are highly sensitive to the components with spatial coherence. Thus, we utilized the principal components of the normal moveout (NMO)-corrected seismic events with different scanning velocities to construct a weighting function. The new function not only has a high resolution for velocity scanning, but it is also a friendly method for the AVO phenomenon. Numerical experiments with the synthetic and field seismic data sets proved that the new method significantly improves resolution and can provide more accurate picked velocities compared with conventional methods.

Real-time dynamic star image registration is essential for star pattern recognition and target tracking. To ensure real-time registration within limited computing resources, a semi-analytical Fourier-Mellin method is introduced for estimating registration parameters. The proposed method uses the position information and connected-domain areas of extracted star points to calculate the Fourier spectra, in contrast to the conventional pixel-level Fourier transform. Consequently, the method demonstrates exceptional computational efficiency and resilience to amplitude fluctuations in deep-space backgrounds. Additionally, an innovative one-dimensional cross-correlation function is proposed for determining registration parameters, significantly reducing the number of search operations and computational complexity compared to traditional two-dimensional functions. Furthermore, a global coarse and local fine positioning strategy is implemented to enhance the framework's efficiency while maintaining registration accuracy. Numerical experiments validate that the proposed scheme constitutes a robust and real-time process for star image registration, making it a practical solution for on-orbit platforms with constrained computing and storage capabilities.

Staggered-grid finite-difference (FD) method is widely used to solve the wave equation for the numerical seismic modeling, and it is a key step of the advanced seismic imaging and inversion problem. However, the conventional FD method is prone to instability and dispersion error due to the insufficient approximation accuracy. In this work, we propose an efficient temporal high-order finite-difference (FD) scheme with the cross-rhombus stencil. Compared with the standard cross-rhombus method, the new method has less computational cost due to we simplify the FD scheme. Moreover, the dispersion relation of the new method is easy to obtain the dispersion-relation-preserving (DRP) FD coefficients, which can significantly alleviate the spatial and temporal dispersion errors. Dispersion and stability analyses indicate that the new scheme has better performance in seismic modeling than the conventional method, and numerical experiments also indicate that the new scheme can effectively mitigate dispersion error and improve the numerical accuracy.