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.

As a degenerative disease, the pathogenesis and treatment of osteoarthritis (OA) are still being studied. The prevailing view is that articular cartilage dysfunction plays an essential role in the development of osteoarthritis. Similarly, dynamic bone remodeling dramatically influences the development of osteoarthritis. The inflammatory response is caused by the overexpression of inflammatory factors, among which tumor necrosis factor-α is one of the main causes of OA, and its sources include the secretion of chondrocytes themselves and osteoclast secretion of subchondral bone. Moreover, TNF-α-induced activation of RIP1, RIP3, and MLKL has been shown to play an important role in cell necroptosis and inflammatory responses. In vitro, AZ-628 alleviates chondrocyte inflammation and necroptosis by inhibiting the NF-κB signaling pathway and RIP3 activation instead of RIP1 activation. AZ-628 also reduces osteoclast activity, proliferation and differentiation, and release of inflammatory substances by inhibiting autophagy, MAPK, and NF-κB pathways. Similarly, the in vivo study demonstrated that AZ-628 could inhibit chondrocyte breakdown and lower osteoclast formation and bone resorption, thereby slowing down subchondral bone changes induced by dynamic bone remodeling and reversing the progression of osteoarthritis in mice. The results of this study indicate that AZ-628 could be used to treat OA byinhibiting chondrocyte necroptosis and regulating osteoclast formation.

Interferometric synthetic aperture radar (InSAR) has developed rapidly over the past years and is considered as an important method for surface deformation monitoring, benefiting from growing data quantities and improving data quality. However, the handing of SAR big data poses significant challenges for related algorithms and pipeline, particularly in large-scale SAR data processing. In addition, InSAR algorithms are highly complex, and their task dependencies are intricate. There is a lack of efficient optimization models and task scheduling for InSAR pipeline. In this paper, we design parallel time-series InSAR processing models based on multi-thread technology for high efficiency in processing InSAR big data. These models concentrate on parallelizing critical algorithms that have high complexity, with a focus on deconstructing two computationally intensive algorithms through loop unrolling. Our parallel models have shown a significant improvement of 10–20 times in performance. We have also developed a parallel optimization tool, Simultaneous Task Automatic Runtime (STAR), which utilizes a data flow optimization strategy with thread pool technology to address the problem of low CPU utilization resulting from multiple modules and task dependencies in the InSAR processing pipeline. STAR provides a data-driven pipeline and enables concurrent execution of multiple tasks, with greater flexibility to keep the CPU busy and further improve CPU utilization through predetermined task flow. Additionally, a supercomputing-based system has been constructed for processing massive InSAR scientific big data and providing technical support for nationwide surface deformation measurement, in accordance with the framework of time series InSAR data processing. Using this system, we processed InSAR data with the volumes of 500 TB and 700 TB in 5 and 7 days, respectively. Finally we generated two maps of land surface deformation all over China.

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.