Division of focal plane (DoFP) polarization imaging sensors have the distinct advantage of acquiring temporally synchronized Stokes vector in one scene. The sensors’ spatially modulated arrangement of a micropolarization array results in loss of spatial resolution and instantaneous field-of-overview errors. Polarization demosaicking (PDM) methods are often utilized to address these drawbacks and achieve the goal of recovering missing polarization information. In this paper, we propose minimized Laplacian polarization residual interpolation for PDM. The Laplacian energy is introduced to improve the interpolation accuracy. We employ interchannel correlation and a guided filter to generate precise tentative estimates and the interpolation performed in the residual domain, where the residuals are the differences between observed values and tentative estimates. Experiments demonstrate that the proposed algorithm provides superior performance in terms of mean average error and peak signal-to-noise ratio.

In a recent article, the authors developed a new optical interferometric telescope architecture based on compressive sensing theory (CS-CPCIT)(Liu et al., MNRAS, 478, 2065, 2018). A new optical interferometric telescope, also known as the Segmented Planar Imaging Detector for Electro-optical Reconnaissance (SPIDER)(Duncan et al., AMOS Conf., 27, 2015), provides a significant reduction in the weight, size and power consumption compared with traditional optical interferometry. The new CS-CPCIT system has a more concise structure and a better spatial frequency sampling capability compared to those of SPIDER. In this paper, we propose an update to CS-CPCIT, which changes the relationship between the number of spatial frequencies sampled and the number of lenslets from linear to quadratic while maintaining a concise structure. Other attractive properties of the update to CS-CPCIT include a high sampling efficiency and a greatly improved maximum number of spatial frequencies that can be sampled.

Human action recognition based on skeleton has played a key role in various computer vision-related applications, such as smart surveillance, human-computer interaction, and medical rehabilitation. However, due to various viewing angles, diverse body sizes, and occasional noisy data, etc., this remains a challenging task. The existing deep learning-based methods require long time to train the models and may fail to provide an interpretable descriptor to code the temporal-spatial feature of the skeleton sequence. In this paper, a key-segment descriptor and a temporal step matrix model are proposed to semantically present the temporal-spatial skeleton data. First, a skeleton normalization is developed to make the skeleton sequence robust to the absolute body size and initial body orientation. Second, the normalized skeleton data is divided into skeleton segments, which are treated as the action units, combining 3D skeleton pose and the motion. Each skeleton sequence is coded as a meaningful and characteristic key segment sequence based on the key segment dictionary formed by the segments from all the training samples. Third, the temporal structure of the key segment sequence is coded into a step matrix by the proposed temporal step matrix model, and the multiscale temporal information is stored in step matrices with various steps. Experimental results on three challenging datasets demonstrate that the proposed method outperforms all the hand-crafted methods and it is comparable to recent deep learning-based methods.

Imaging past the diffraction limit is of significance to an optical system. Fourier ptychography (FP) is a novel coherent imaging technique that can achieve this goal and it is widely used in microscopic imaging. Most phase retrieval algorithms for FP reconstruction are based on Gaussian measurements which cannot extend straightforwardly to long range, sub-diffraction imaging setup because of laser speckle noise corruption. In this work, a new FP reconstruction framework is proposed for macroscopic visible imaging. When compared with existing research, the reweighted amplitude flow algorithm is adopted for better signal modeling, and the Regularization by Denoising (RED) scheme is introduced to reduce the effects of speckle. Experiments demonstrate that the proposed method can obtain state-of-the-art recovered results on both visual and quantitative metrics without increasing computation cost, and it is flexible for real imaging applications.

As an important error in star centroid location estimation, the systematic error greatly restricts the accuracy of the three-axis attitude supplied by a star sensor. In this paper, an analytical study about the behavior of the systematic error in the center of mass (CoM) centroid estimation method under different Gaussian widths of starlight energy distribution is presented by means of frequency field analysis and numerical simulations. Subsequently, an optimized extreme learning machine (ELM) based on the bat algorithm (BA) is adopted to predict the systematic error of the actual star centroid position and then compensate the systematic error from the CoM method. In the BA-ELM model, the input weights matrix and hidden layer biases parameters are encoded as microbat’s locations and optimized by utilizing the strong global search capacity of BA, which significantly improves the performance of ELM in terms of prediction accuracy. The simulation result indicates that our method can reduce the systematic error to less than 3.0 × 10⁻⁷ pixels, and its compensation accuracy is two or three orders of magnitude higher than that of other methods for estimating a star centroid location under a 3 × 3 pixel sampling window.

Imaging correlography, an effective method for long-distance imaging, recovers an object using only the knowledge of the Fourier modulus, without needing phase information. It is not sensitive to atmospheric turbulence or optical imperfections. However, the unreliability of traditional phase retrieval algorithms in imaging correlography has hindered their development. In this work, we join imaging correlography and ptychography together to overcome such obstacles. Instead of detecting the whole object, the object is measured part-by-part with a probe moving in a ptychographic way. A flexible optimization framework is proposed to reconstruct the object rapidly and reliably within a few iterations. In addition, novel image space denoising regularization is plugged into the loss function to reduce the effects of input noise and improve the perceptual quality of the recovered image. Experiments demonstrate that four-fold resolution gains are achievable for the proposed imaging method. We can obtain satisfactory results for both visual and quantitative metrics with one-sixth of the measurements in the conventional imaging correlography. Therefore, the proposed imaging technique is more suitable for long-range practical applications.