Infrared super-resolution imaging based on compressed sensing

The theoretical basis of traditional infrared super-resolution imaging method is Nyquist sampling theorem. The reconstruction premise is that the relative positions of the infrared objects in the low-resolution image sequences should keep fixed and the image restoration means is the inverse operation of ill-posed issues without fixed rules. The super-resolution reconstruction ability of the infrared image, algorithm’s application area and stability of reconstruction algorithm are limited. To this end, we proposed super-resolution reconstruction method based on compressed sensing in this paper. In the method, we selected Toeplitz matrix as the measurement matrix and realized it by phase mask method. We researched complementary matching pursuit algorithm and selected it as the recovery algorithm. In order to adapt to the moving target and decrease imaging time, we take use of area infrared focal plane array to acquire multiple measurements at one time. Theoretically, the method breaks though Nyquist sampling theorem and can greatly improve the spatial resolution of the infrared image. The last image contrast and experiment data indicate that our method is effective in improving resolution of infrared images and is superior than some traditional super-resolution imaging method. The compressed sensing super-resolution method is expected to have a wide application prospect.     

X光环孔编码成像技术研究

 着眼于惯性约束聚变实验研究中的应用，对X光环孔编码成像技术进行了研究。利用数值模拟的手段，分析了环孔的自相关函数及其对环孔编码成像技术图像重建的影响，对不同的图像重建技术进行了初步比较。基于星光II实验装置，利用自行研制的X光环孔编码成像系统进行了激光等离子体X光环孔编码成像技术的演示实验，获得了满意结果。     

Analysis of the features and reconstruction of a high resolution infrared image based on a multi-aperture imaging system

Infrared images of good quality are strictly important for such applications as targets detection, tracking and identifying. Traditional single aperture infrared imaging system brings in some defects for its imaging scheme. Multi-aperture imaging system shows promising characteristic of improving image quality and reducing size of optical instruments. We reconstruct a high resolution infrared image from the low resolution sub-images collected by the compact multi-aperture imaging system. A novel reconstruction method called pixels closely arrange (PCA) is proposed based on analyzing the compound eye imaging process, and this method is verified in a simulated 3D infrared scene to capture sub-images. An evaluation of the reconstructed image quality is presented to discuss the significant factors that affect the final result. Experimental results show that the PCA method can be efficiently applied to the multi-aperture infrared imaging system as long as the structure of the micro-lens array is specifically designed to be adaptive to the infrared focal plane array (IFPA).     

Infrared super-resolution imaging method based on retina micro-motion

With the wide application of infrared focal plane arrays (IRFPA), military, aerospace, public security and other applications have higher and higher requirements on the spatial resolution of infrared images. However, traditional super-resolution imaging methods have increasingly unable to meet this requirement in technology. In this paper, we adopt the achievement that the human retina micro-motion is the important reason why the human has the hyperacuity ability. Based on the achievement, we bring forward an infrared super-resolution imaging method based on retina micro-motion. In the method, we use the piezoelectric ceramic equipment to control the infrared detector moving variably within a plane parallel to the focal plane. The motion direction is toward each other into a direction of 90°. In the four directions of the movement, we get four sub-images and generate a high spatial resolution infrared image by image interpolation method. In the process of the shifting movement of the detector, we set the threshold of the detector response and record the response time difference when adjacent pixel responses are up to the threshold. By the method, we get the object’s edges, enhance them in the high resolution infrared image and get the super-resolution infrared image. The experimental results show that our proposed super-resolution imaging methods can improve the spatial resolution of the infrared image effectively. The method will offer a new idea for the super-resolution reconstruction of infrared images.     

孔径编码近场效应删除的新方法

自从孔径编码技术在X射线天文观测上的成功应用以来,其下一步的发展方向已经转移到在医学成像中的应用. 孔径编码技术的特性之一是它可以在不严重损害分辨率的情况下大大提高探测灵敏度(缩短探测时间). 由此孔径编码近场成像中的图像畸变的消除成为待解决的问题. 在各种码板中MURA(均匀冗余阵列的变种)被讨论和使用得最多. 而“Roberto Arrcosi” 发明了一种使用正反两块MURA码板消近场畸变得技术. 本文中我们提出两种新方法消除近场畸变,一种基于他的工作,一种基于医学成像的MLEM算法.     

Approaches to eliminate near field artifact of MURA

Since the coded aperture technique has been successfully applied on X-ray imaging space telescopes, attentions of its development have also been cast on the application in medical imaging, for it has a very tempting quality to greatly enhance the detection sensitivity without gravely lowering the spacial resolution. But when the coded aperture technique is applied to image a nearby object, the so called "near-field artifact"comes up, that is, the reconstructed image has a sort of distortion. Among types of coded apertures the MURA (Modified Uniformly Redundant Array) is one of the most discussed. Roberto Arrcosi came up with the solution to remove the artifacts utilizing mask and antimask. In this article we present two ways to eliminate the second order aberration based on his work.     

Single image non-uniformity correction using compressive sensing

A non-uniformity correction (NUC) method for an infrared focal plane array imaging system was proposed. The algorithm, based on compressive sensing (CS) of single image, overcame the disadvantages of “ghost artifacts” and bulk calculating costs in traditional NUC algorithms. A point-sampling matrix was designed to validate the measurements of CS on the time domain. The measurements were corrected using the midway infrared equalization algorithm, and the missing pixels were solved with the regularized orthogonal matching pursuit algorithm. Experimental results showed that the proposed method can reconstruct the entire image with only 25% pixels. A small difference was found between the correction results using 100% pixels and the reconstruction results using 40% pixels. Evaluation of the proposed method on the basis of the root-mean-square error, peak signal-to-noise ratio, and roughness index (ρ) proved the method to be robust and highly applicable.     

惯性约束聚变实验中编码成像的反投影解码

 描述了惯性约束聚变实验中编码成像原理及解码方法。以环孔显微镜为例,对ICF实验中编码成像的解码进行数字研究。采用反投影算法进行数值解码,并编制数值解码程序,对两个实例进行模拟计算。模拟结果表明该算法的图像解码复原精度高 ,效果好。     

An infrared image super-resolution reconstruction method based on compressive sensing

Limited by the properties of infrared detector and camera lens, infrared images are often detail missing and indistinct in vision. The spatial resolution needs to be improved to satisfy the requirements of practical application. Based on compressive sensing (CS) theory, this thesis presents a single image super-resolution reconstruction (SRR) method. With synthetically adopting image degradation model, difference operation-based sparse transformation method and orthogonal matching pursuit (OMP) algorithm, the image SRR problem is transformed into a sparse signal reconstruction issue in CS theory. In our work, the sparse transformation matrix is obtained through difference operation to image, and, the measurement matrix is achieved analytically from the imaging principle of infrared camera. Therefore, the time consumption can be decreased compared with the redundant dictionary obtained by sample training such as K-SVD. The experimental results show that our method can achieve favorable performance and good stability with low algorithm complexity.     

单幅同轴全息图两步迭代收缩重建

利用压缩传感理论中的两步迭代收缩重建算法,开展单幅同轴全息图重建实验研究,实现单幅同轴全息图共轭重建像的消除并克服数字全息技术在轴向聚焦平面识别能力的不足。以数字图像和标准分辨率板为记录物体,比较分析了基于两步迭代收缩算法和菲涅尔近似衍射重建算法的重建质量;以两根裸光纤为实验样本,分析了两步迭代收缩重建算法对记录物体轴向不同焦平面的识别能力。实验结果表明两步迭代收缩重建算法可得到清晰度高于68.73%的重建信息,同时对直径为125 m的两根光纤在9 mm的轴向间距条件下,显示出了比全息菲涅尔近似算法更好的聚焦平面识别能力。这一轴向聚焦识别能力有助于数字全息技术应用于功能材料梯度参数或功能涂层光学器件涂层厚度检测。     

Optimization of sparse synthetic transmit aperture imaging with coded excitation and frequency division

An effective aperture approach is used for optimization of a sparse synthetic transmit aperture (STA) imaging system with coded excitation and frequency division. A new two-stage algorithm is proposed for optimization of both the positions of the transmit elements and the weights of the receive elements. In order to increase the signal-to-noise ratio in a synthetic aperture system, temporal encoding of the excitation signals is employed. When comparing the excitation by linear frequency modulation (LFM) signals and phase shift key modulation (PSKM) signals, the analysis shows that chirps are better for excitation, since at the output of a compression filter the sidelobes generated are much smaller than those produced by the binary PSKM signals. Here, an implementation of a fast STA imaging is studied by spatial encoding with frequency division of the LFM signals. The proposed system employs a 64-element array with only four active elements used during transmit. The two-dimensional point spread function (PSF) produced by such a sparse STA system is compared to the PSF produced by an equivalent phased array system, using the Field II simulation program. The analysis demonstrates the superiority of the new sparse STA imaging system while using coded excitation and frequency division. Compared to a conventional phased array imaging system, this system acquires images of equivalent quality 60 times faster, when the transmit elements are fired in pairs consecutively and the power level used during transmit is very low. The fastest acquisition time is achieved when all transmit elements are fired simultaneously, which improves detectability, but at the cost of a slight degradation of the axial resolution. In real-time implementation, however, it must be borne in mind that the frame rate of a STA imaging system depends not only on the acquisition time of the data but also on the processing time needed for image reconstruction. Comparing to phased array imaging, a significant increase in the frame rate of a STA imaging system is possible if and only if an equivalent time efficient algorithm is used for image reconstruction.     

压缩感知光谱成像技术的编码孔径与探测器匹配优化

编码孔径光谱成像仪在实际应用中存在着编码模板与探测器分辨率不匹配从而降低系统分辨率的问题。针对该问题进行了两种情况分析,并通过数学理论建模给出了相应的解决方案。对于编码模板分辨率高于探测器分辨率这一情况,提出引入邻域嵌入超分辨技术的方法,实现了基于压缩感知的超分辨光谱成像。对于编码模板分辨率低于探测器分辨率这一情况,提出区块阈值划分的编码孔径,将编码微元按照区块阈值重新划分并进行灰度分级,从而实现低分辨率编码模板的高分辨率编码孔径。利用梯度投影稀疏重构(GPSR)算法进行数据立方体重建,实验结果表明:运用基于超分辨理论的编码孔径快照光谱成像系统所测得的光谱图像更精准,内容更丰富;采用基于区块阈值划分的编码孔径的编码孔径快照光谱成像系统具有更高的空间分辨率和光谱分辨率。结果证实优化后的编码孔径快照光谱成像系统,其分辨率和成像质量大幅度提升,并实现了高分辨率元件的100%利用。     

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.     

光子集成干涉成像系统微透镜排布设计与图像复原

光子集成干涉成像系统体积小、重量轻、功耗小,且系统分辨率不受单个透镜口径尺寸的限制,是一种新兴的成像技术.针对光子集成干涉成像系统图像复原问题,开展了图像复原技术和微透镜阵列最优排布研究,提出了基于压缩感知的光子集成干涉成像图像恢复技术,以及基于图像残差的最优微透镜阵列排布设计评估方法;通过计算仿真,可实现在有限空间体...     

极值温差红外图像的自适应双局部增强算法

针对目前红外焦平面成像系统在观察目标、特别是极值温差目标时,各温度段灰度描述不均匀和细节不够的问题,提出了一种自适应红外图像双局部增强算法。详细介绍了通过空间分布和灰度统计特性两个方向实现对极值温差图像自适应增强的方法,该方法首先从红外图像的空间分布特性出发,将图像切割成多个局部图像,然后再从直方图灰度分布出发,将局部图像的直方图进行聚类分段,并对分段直方图均衡增强,最后对生成的每个局部图像增强结果进行线性插值拼接完成增强算法。通过在红外焦平面系统中实验证明了极值温差自适应的红外图像双局部增强算法的可行性,并获得了很好的效果,成像质量有明显提高。     

X射线编码孔径成像中的一种高精度图像重构方法

在惯性约束聚变实验的过程诊断研究中,采用环形编码孔径成像技术可以同时获得高的空间分辨力和时间分辨力。获得高的空间分辨力的关键之一是如何准确地获得目标的点扩展函数。通常采用直接投影法。但这种方法忽略了X射线的衍射效应,因此限制了分辨力的提高。依据光的标量衍射理论,考虑X射线的衍射效应,导出了环形编码孔径的点扩展函数。并在此基础上制作了维纳滤波器。在激光等离子体重点实验室,用内直径为250μm,外直径为260μm的环形孔径板,对惯性约束聚变（ICF）的过程进行成像实验,得到了靶标的编码图像。采用以衍射为基础的维钠滤波器进行重构,获得的重构图像明显的优于用直接投影法得到的结果。     

Super-resolution imaging via sparsity constraint and sparse speckle illumination

We present an imaging approach via sparsity constraint and sparse speckle illumination which can dramatically enhance the optical system's imaging resolution. When the object is illuminated by some sparse speckles and the sparse reconstruction algorithm is utilized to restore the blur image, numerical simulated results demonstrate that the image,whose resolution exceeds the Rayleigh limit, can be stably reconstructed even if the detection signal-to-noise ratio(SNR)is less than 10 d B. Factors affecting the quality of the reconstructed image, such as the coded pattern's sparsity and the detection SNR, are also studied.     

Research on spatial-variant property of bistatic ISAR imaging plane of space target

The imaging plane of inverse synthetic aperture radar(ISAR) is the projection plane of the target. When taking an image using the range-Doppler theory, the imaging plane may have a spatial-variant property, which causes the change of scatter’s projection position and results in migration through resolution cells. In this study, we focus on the spatial-variant property of the imaging plane of a three-axis-stabilized space target. The innovative contributions are as follows. 1) The target motion model in orbit is provided based on a two-body model. 2) The instantaneous imaging plane is determined by the method of vector analysis. 3) Three Euler angles are introduced to describe the spatial-variant property of the imaging plane, and the image quality is analyzed. The simulation results confirm the analysis of the spatial-variant property. The research in this study is significant for the selection of the imaging segment, and provides the evidence for the following data processing and compensation algorithm.     

A novel method of high resolution imaging through random perturbation wave front

A novel method is proposed for eliminating the effect of the traditional optical aberration on high resolution imaging by random perturbation wave front and digital image processing. A random phase mask, whose phase spectrum fluctuation is accordant with Kolmogorov distribution, is positioned near the aperture stop of optical system, making the optical aberration image become random perturbation image, that is, the intermediate image. The blind deconvolution algorithm based on maximum-likelihood estimation technique is used to restore the intermediate image acquired by a digital detector. The effects of optical aberrations and the noise on the restoration image are explored. To demonstrate the validity of the method proposed, the computer simulation and laboratory experiments are carried out for the imaging of the optical system with primary aberration. The results have shown that the present method is well suited for effectively improving the imaging quality of the optical system with certain aberration, thus making the optical system resolution close to or reach the diffraction-limit of the optical system.     

Terahertz imaging with compressed sensing and phase retrieval

We describe a novel, high-speed pulsed terahertz (THz) Fourier imaging system based on compressed sensing (CS), a new signal processing theory, which allows image reconstruction with fewer samples than traditionally required. Using CS, we successfully reconstruct a 64 x 64 image of an object with pixel size 1.4 mm using a randomly chosen subset of the 4096 pixels, which defines the image in the Fourier plane, and observe improved reconstruction quality when we apply phase correction. For our chosen image, only about 12% of the pixels are required for reassembling the image. In combination with phase retrieval, our system has the capability to reconstruct images with only a small subset of Fourier amplitude measurements and thus has potential application in THz imaging with cw sources.