Frequency modulation continuous wave (FMCW) Lidar inevitably suffers from vibration and nonlinear frequency modulation, which influences the ranging and imaging results. In this paper, we analyze the impact of vibration error coupled with nonlinearity error on ranging for FMCW Lidar, and propose a purely theoretical approach that simultaneously compensates for time-varying vibration and nonlinearity in one-period triangular FMCW (T-FMCW) signals. We first extract the localized characteristics of dechirp signals in time-frequency domain by using a second-order synchro-squeezing transform (second-order SST), and establish an instantaneous ranging model based on second-order SST which can characterize the local distributions of time-varying errors. Second, we estimate the nonlinearity error by using time-frequency information of an auxiliary channel and then preliminarily eliminate the error from the instantaneous measurement range. Finally, we construct a particle filtering (PF) model for T-FMCW using the instantaneous ranging model to compensate for the time-varying vibration error and the residual nonlinearity error, and calculate the range of target by using triangular symmetry relations of T-FMCW. Experimental tests prove that the proposed method can accurately estimate the range of target by compensating for the time-varying vibration and the nonlinearity errors simultaneously in one-period T-FMCW signal.

Subsurface scatters are sometimes masked by reflectors in seismic migration images, because the diffractions are much weaker in energy than the reflections. We propose a novel imaging method, named reflection-damped plane-wave least-squares reverse time migration (RD_PLSRTM), to enhance the scatters in the migration image. We formulate seismic imaging as an inverse problem that minimizes a weighted residual between the modeled and observed seismic data. In the proposed approach, we use the plane-wave destruction filter to separate the diffractions from the reflections in the data residual. A reflection-damped weighting matrix is then used to govern the fitting of the diffractions and the reflections, and therefore emphasize the updates of the scatters. The inverse problem is finally solved by using an iteratively reweighted least-squares (IRLS) algorithm. The proposed method provides a generalized formulation that could be reduced to conventional PLSRTM and PLSRTM of diffractions (PLSRTM_D) by using specific damping factors. We conduct imaging tests on synthetic and field data that prove the superiority of the proposed method over PLSRTM in imaging deep scatters and subsalt scatters. Compared with PLSRTM_D, it could produce high-quality images of not only the scatters but also the reflectors.

Frequency-modulation continuous-wave (FMCW) Laser radar (Ladar) signals may be polluted by noise, which reduces the recognition precision of the targets. This letter proposes an ensemble empirical modal decomposition (EEMD) denoising method with singular spectrum constraint for FMCW Ladar signals. In our approach, we apply EEMD to adaptively decompose the noisy FMCW Ladar signals into several intrinsic mode functions (IMFs) and decompose these IMFs into several singular value components by using the singular spectrum analysis. The singular values are then used to calculate the energy probability of each IMF, which serves as an indicator to detect the IMFs containing useful signals. The energy probability represents the coherence of the IMFs such that we could sort these IMFs into noisy and useful components according to their coherence differences. To suppress the residual noise that is interfered with the selected IMFs, we use a low-rank approximation to reconstruct the useful signals. Finally, the reconstructed IMFs are stacked together to obtain the denoised signals. Tests on synthetic and real data demonstrate that the proposed method, compared to the EEMD denoising method, could suppress more noise but filter out less useful signals in the FMCW Ladar signal denoising.

Angle-domain common image gathers (ADCIGs) describe the reflectivity variation over reflection angles which are important for seismic exploration. The ADCIGs could be obtained using reverse time migration (RTM). However, because of the limitation of seismic frequency band and acquisition geometry, RTM produces the ADCIGs with low fidelity. We propose a reflection angle-domain pseudoextended least-squares RTM method to improve the quality of the ADCIGs in which the Poynting vector is used to efficiently calculate the angles. We first extend the inverted model in the reflection angle domain and derive a feasible pseudoextended Born modeling operator which can map the reflection angle-domain extended model to seismic data. The modeling operator, associated with an adjoint operator, are then used to build a pseudoextended linearized inversion framework for inverting the angle-dependent reflectivity. Taking advantage of the simplicity and coherency of the ADCIGs, we impose a series of low-rank constraints on the extended angle dimension and a sparse constraint on the whole dimension to ensure the production of high-quality ADCIGs. The proposed method is finally solved by a regularized conjugate gradient algorithm. We conduct several numerical examples on a flat layer model, the Marmousi model, and the Sigsbee2A salt model to test the validity and superiority of the proposed method. The results demonstrate that the proposed method could produce the ADCIGs and the stacked image with higher fidelity than RTM, which provides a reliable input for migration velocity analysis, anisotropic model building, and amplitude versus angle analysis.

Few-Shot Industrial Anomaly Detection (FS-IAD) has drawn great attention most recently since data efficiency and the ability to design algorithms for fast migration across products have become the main concerns. The difficulty of memory-based IAD in low-data regime primarily lies in inefficient measurement between the memory bank and query images. We address such a pivotal issue from a new perspective of optimal matching between features of image regions. Taking the unbalanced nature of query features into consideration, we adopt Conditional Transport (CT) as a metric to compute the structural distance between representations of the two sets to determine feature relevance. CT distance generates the optimal matching flows between unbalanced structural elements that achieve the minimum matching cost, which can be directly used for IAD since it well reflects the differences of query images compared with the normal memory. Realizing the fact that query images usually come one-by-one or batch-by-batch, we further propose an Online Conditional Transport (OCT) by making full use of the current and historical query images for IAD via simultaneously calibrating the memory bank using the online query images and matching features between the calibrated memory and the current query image. Go one step further, for sparse foreground products, we employ a predominant segment model to implement Foreground-aware OCT (FOCT) for improving the effectiveness and efficiency of OCT by forcing the model to pay more attention to diverse targets rather than redundant backgrounds when calibrating the memory bank. FOCT can improve the diversity of calibrated memory, which is critical for robust FS-IAD in practice. Besides, FOCT is flexible since it can be friendly plugged and played with any pre-trained backbones, such as WRN, and any pre-trained segment models, such as SAM. The effectiveness and efficiency of our model is demonstrated across diverse datasets, including benchmarks of MVTec and MPDD, achieving SOTA performance.