Sparse clutter nulling space–time adaptive processing (STAP) methods achieve superior performance in the case of limited samples, so they are suitable for nonhomogeneous clutter environments. Traditional sparse clutter nulling STAP algorithms try to estimate the clutter subspace by selecting a suitable set of space–time steering vectors (atoms) in spatial-Doppler profile grids. However, the off-grid effect is inevitable for the nonside-looking case due to the nonlinear distribution of clutter and, thus, leads to significant performance degradation. To solve this problem, a gridless sparse clutter nulling STAP algorithm based on the particle swarm optimization (PSO) named PSO-STAP is proposed in this letter. A criterion function to evaluate the suitability of atoms is first devised. By regarding the criterion as a fitness function, PSO-STAP can select atoms in gridless spatial-Doppler profile, which is more suitable to construct the accurate clutter subspace. Numerical results verified the feasibility and superiority of the proposed algorithm.

The emerging multispectral-filter-array (MSFA) cameras require generalized demosaicing for MSFA engineering. The existing interpolation, compressive sensing and deep learning based methods suffer from either limited reconstruction accuracy or poor generalization. In this work, we report a generalized demosaicing method with structural and adaptive nonlocal optimization, enabling boosted reconstruction accuracy for different MSFAs. The advantages lie in the following three aspects. First, the nonlocal low-rank optimization is applied and extended to the multiple spatial-spectral-temporal dimensions to exploit more crucial details. Second, the block matching accuracy is promoted by employing a novel structural similarity metric instead of the conventional Euclidean distance. Third, the running efficiency is boosted by an adaptive iteration strategy. We built a prototype system to capture raw mosaic images under different MSFAs, and used the technique as an off-the-shelf tool to demonstrate MSFA engineering. The experiments show that the binary tree (BT) based filter array produces higher accuracy than the random and regular ones for different number of channels.

Images captured in rainy conditions are often corrupted by unexpected rain streaks, which severely degrade the performance of subsequent processes in outdoor computer vision systems. In this paper, we exploit the directional smoothness of rain streaks for the single-image rain streaks removal and propose a convex model that uses the directional total variation (DTV) to characterize the smoothness of rain streaks in arbitrary orientations. The proposed model consists of four terms: the fidelity term, the ℓ1norm for the sparsity of rain streaks, and two DTV regularization terms for the directional smoothness and the piecewise smoothness of rain streaks and rain-free backgrounds, respectively. To solve the proposed model, we develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) framework. Extensive experimental results on both synthetic and real rainy images show that our method outperforms the recent state-of-the-art methods visually and quantitatively.

We address the problem of high-rank matrix completion with side information. In contrast to existing work dealing with side information, which assume that the data matrix is low-rank, we consider the more general scenario where the columns of the data matrix are drawn from a union of low-dimensional subspaces, which can lead to a high rank matrix. Our goal is to complete the matrix while taking advantage of the side information. To do so, we use the self-expressive property of the data, searching for a sparse representation of each column of matrix as a combination of a few other columns. More specifically, we propose a factorization of the data matrix as the product of side information matrices with an unknown interaction matrix, under which each column of the data matrix can be reconstructed using a sparse combination of other columns. As our proposed optimization, searching for missing entries and sparse coefficients, is non-convex and NP-hard, we propose a lifting framework, where we couple sparse coefficients and missing values and define an equivalent optimization that is amenable to convex relaxation. We also propose a fast implementation of our convex framework using a Linearized Alternating Direction Method. By extensive experiments on both synthetic and real data, and, in particular, by studying the problem of multi-label learning, we demonstrate that our method outperforms existing techniques in both low-rank and high-rank data regimes.

Cell polarization toward an attractant is influenced by both physical and chemical factors. Most existing mathematical models are based on reaction-diffusion systems and only focus on the chemical process occurring during cell polarization. However, membrane tension has been shown to act as a long-range inhibitor of cell polarization. Here, we present a cell polarization model incorporating the interplay between Rac GTPase, filamentous actin (F-actin), and cell membrane tension. We further test the predictions of this model by performing single cell measurements of the spontaneous polarization of cancer stem cells (CSCs) and non-stem cancer cells (NSCCs), as the former have lower cell membrane tension. Based on both our model and the experimental results, cell polarization is more sensitive to stimuli under low membrane tension, and high membrane tension improves the robustness and stability of cell polarization such that polarization persists under random perturbations. Furthermore, our simulations are the first to recapitulate the experimental results described by Houk et al., revealing that aspiration (elevation of tension) and release (reduction of tension) result in a decrease in and recovery of the activity of Rac-GTP, respectively, and that the relaxation of tension induces new polarity of the cell body when a cell with the pseudopod-neck-body morphology is severed.