Hybrid pupil filter design using Bessel series

We propose a simple method of designing pupil filters for transverse super-resolution. For this end we represent the amplitude Point Spread Function (PSF) as a series expansion, constructed from the Fourier transform of a basis of Bessel functions. With this representation we optimize the intensity PSF according to certain desired characteristics, such as a smaller disk diameter than the corresponding, clear aperture, Airy disk. It is proved that by using few basis functions, it is possible to design pupils with similar or better PSF characteristics than previously reported.     

Single infrared image super-resolution combining non-local means with kernel regression

In many infrared imaging systems, the focal plane array is not sufficient dense to adequately sample the scene with the desired field of view. Therefore, there are not enough high frequency details in the infrared image generally. Super-resolution (SR) technology can be used to increase the resolution of low-resolution (LR) infrared image. In this paper, a novel super-resolution algorithm is proposed based on non-local means (NLM) and steering kernel regression (SKR). Based on that there are a large number of similar patches within an infrared image, NLM method can abstract the non-local similarity information and then the value of high-resolution (HR) pixel can be estimated. SKR method is derived based on the local smoothness of the natural images. In this paper the SKR is used to give the regularization term which can restrict the image noise and protect image edges. The estimated SR image is obtained by minimizing a cost function. In the experiments the proposed algorithm is compared with state-of-the-art algorithms. The comparison results show that the proposed method is robust to the noise and it can restore higher quality image both in quantitative term and visual effect.     

Stable separation and super-resolution of mixture models

We consider simultaneously identifying the membership and locations of point sources that are convolved with different band-limited point spread functions, from the observation of their superpositions. This problem arises in three-dimensional super-resolution single-molecule imaging, neural spike sorting, multi-user channel identification, among other applications. We propose a novel algorithm, based on convex programming, and establish its near-optimal performance guarantee for exact recovery in the noise-free setting by exploiting the spectral sparsity of the point source models as well as the incoherence between point spread functions. Furthermore, robustness of the recovery algorithm in the presence of bounded noise is also established. Numerical examples are provided to demonstrate the effectiveness of the proposed approach.     

Testing Gaussian process with applications to super-resolution

This article introduces exact testing procedures on the mean of a Gaussian process X derived from the outcomes of ${\ell }_1$-minimization over the space of complex valued measures. The process X can be thought as the sum of two terms: first, the convolution between some kernel and a target atomic measure (mean of the process); second, a random perturbation by an additive centered Gaussian process. The first testing procedure considered is based on a dense sequence of grids on the index set of X and we establish that it converges (as the grid step tends to zero) to a randomized testing procedure: the decision of the test depends on the observation X and also on an independent random variable. The second testing procedure is based on the maxima and the Hessian of X in a grid-less manner. We show that both testing procedures can be performed when the variance is unknown (and the correlation function of X is known). These testing procedures can be used for the problem of deconvolution over the space of complex valued measures, and applications in frame of the Super-Resolution theory are presented. As a byproduct, numerical investigations may demonstrate that our grid-less method is more powerful (it detects sparse alternatives) than tests based on very thin grids.     

Modern optical microscopy methods to study biomolecular condensates

Cells achieve highly intricate internal organization via membrane-bound and membraneless organelles. Research over the past decade has implicated liquid–liquid phase separation, a phenomenon by which charged and often disordered biological macromolecules assemble into reversible liquid-like condensates, as the mechanism of formation of membraneless organelles in cells. During the same period, optical microscopy saw exciting advancements, including the super-resolution revolution, that were quickly adopted by researchers in the biological community. Today, there exists a vast library of techniques capable of providing unprecedented information regarding the formation, function, and dynamics of biomolecular condensates. In this review, we discuss a select number of modern optical microscopy methods that are particularly suited for studying biomolecular condensates both in vitro and in cells, as well as the associated technical challenges.     

基于MAP的高光谱图像超分辨率方法

高光谱图像得到了越来越广泛的应用,但较低的空间分辨率严重地影响着它的应用效果;其超分辨率方法受到学术界的高度重视,但一直没有得到很好的解决。为此重点研究了建立低分辨率资源图像与高分辨率目标图像之间的关系模型;引入关联感兴趣光谱端元的算子进行空间变换;应用最大后验概率(MAP)算法实现超分辨率复原。实验表明,该超分辨率方法具有超分辨率效果好、复杂度低、抗噪声性能强和保护感兴趣类别等优点。     

基于Powell算法的亚像元配准方法

基于Powell优化算法和图像的金字塔分解,提出了一个新的亚像元运动参数估计方法.用参考图像和待配准图像构造的非线性最小二乘公式作为Powell算法的目标函数,并且以运动参数作为变量.由细到粗的多分辨率分解为准确的亚像元参数估计提供了基础.数值实验结果表明,该算法可以用于准确的稳健的亚像元参数估计,其不足之处在于运行时间比较长.     

一种新的运动模糊图像超分辨力盲复原算法

研究了具有深度运动模糊效果的图像的复原算法.采用对运动模糊图像的傅里叶频谱进行Radon变换来估计运动模糊方向,在此方向上计算运动模糊图像的自相关来估计运动模糊长度,再以运动模糊方向和运动模糊长度为参量结合超分辨力图像复原处理算法对比较严重的运动模糊图像进行复原.结果表明,该综合性算法能够较为精确地估算出运动模糊图像的模糊参量并取得较好的复原效果.     

A novel architecture for a super-resolution PIV algorithm developed for the improvement of the resolution of large velocity gradient measurements

Three different particle image processing algorithms have been developed for the improvement of PIV velocity measurements characterized by large velocity gradients. The objectives of this study are to point out the limitations of the standard processing methods and to propose a complete algorithm to enhance the measurement accuracy. The heart of the PIV image processing is a direct cross-correlation calculation in order to obtain complete flexibility in the choice of the size and the shape of the interrogation window (IW). An iterative procedure is then applied for the reduction of the size of IW at each measurement location. This procedure allows taking into account the local particle concentration in the image. The results of this first iterative processing, applied to synthetic images, show both a significant improvement of measurement accuracy and an increase of the spatial resolution. Finally, a super-resolution algorithm is developed to further increase the spatial resolution of the measurement by determining the displacement of each particle. The computer time for a complete image processing is optimized by the introduction of original data storage in Binary Space Partitions trees. It is shown that measurement errors for large velocity gradient flows are similar to those obtained in simpler cases with uniform translation displacements. This last result validates the ability of the developed super-resolution algorithm for the aerodynamic characterization of large velocity gradient flows.     

An improved POCS super-resolution infrared image reconstruction algorithm based on visual mechanism

The traditional projection onto convex sets (POCS) super-resolution (SR) reconstruction algorithm can only get reconstructed images with poor contrast, low signal-to-noise ratio and blurring edges. In order to solve the above disadvantages, an improved POCS SR infrared image reconstruction algorithm based on visual mechanism is proposed, which introduces data consistency constraint with variable correction thresholds to highlight the target edges and filter out background noises; further, the algorithm introduces contrast constraint considering the resolving ability of human eyes into the traditional algorithm, enhancing the contrast of the image reconstructed adaptively. The experimental results show that the improved POCS algorithm can acquire high quality infrared images whose contrast, average gradient and peak signal to noise ratio are improved many times compared with traditional algorithm.