Simultaneous degradation estimation and restoration of confocal images and performance evaluation by colocalization analysis

A novel method for joint restoration and estimation of the degradation of confocal microscope images is presented. The observed images are degraded due to two sources: blurring due to the band-limited nature of the optical system [modelled by the point spread function (PSF)], and Poisson noise contaminates the observations due to the discrete nature of the photon detection process. The proposed method iterates noise reduction, blur estimation and deblurring, and applies these steps in two phases, i.e. a training phase and a restoration phase. In the first phase, these three steps are iterated until the blur estimation converges. Noise reduction and blur estimation are performed using steerable pyramids, and the deblurring is performed by the Richardson–Lucy algorithm. The second phase is the actual restoration. From then on, the blur estimation is used as a criterion to measure the image quality. The iterations are stopped when this measure converges, a result that is guaranteed. The integrated method is completely automatic, and no prior information on the image is required. The method has been given the name SPERRIL (Steerable Pyramid-based Estimation and Regularized Richardson–Lucy restoration). Compared with existing techniques by both objective measures and visual observation, in the SPERRIL-restored images noise is better suppressed.     

Performance evaluation of image segmentation algorithms on microscopic image data

In our paper, we present a performance evaluation of image segmentation algorithms on microscopic image data. In spite of the existence of many algorithms for image data partitioning, there is no universal and ‘the best’ method yet. Moreover, images of microscopic samples can be of various character and quality which can negatively influence the performance of image segmentation algorithms. Thus, the issue of selecting suitable method for a given set of image data is of big interest. We carried out a large number of experiments with a variety of segmentation methods to evaluate the behaviour of individual approaches on the testing set of microscopic images (cross‐section images taken in three different modalities from the field of art restoration). The segmentation results were assessed by several indices used for measuring the output quality of image segmentation algorithms. In the end, the benefit of segmentation combination approach is studied and applicability of achieved results on another representatives of microscopic data category – biological samples – is shown.     

加权Schatten范数低秩表示的高光谱图像恢复

高光谱图像在获取过程中常受到多种类型噪声的干扰,如高斯噪声、脉冲噪声、条纹噪声等,为确保后续应用的顺利进行,提出了一种基于加权Schatten范数低秩表示的高光谱图像恢复方法。该方法引入低秩表示模型恢复高光谱数据,采用加权Schatten范数代替核函数,更精确地逼近秩函数;并选用初步无噪图像作为低秩表示的字典,进一步提高了模型对图像的恢复能力。另外,引入拉普拉斯正则项刻画数据内部的几何结构,能保持图像的细节。模拟和实际高光谱数据的实验结果表明,较多种相关的方法在视觉效果和量化指标值都有很大的改进。与经典的基于低秩先验的恢复方法相比,本文算法的平均峰值信噪比提高2.74 dB,平均结构相似性数值指标提高0.03,而平均光谱角能降低1.40。新模型不仅能充分利用高光谱图像光谱维的低秩先验,而且保持了数据内部的几何结构,有利于恢复出高质量的清晰图像。     

MRT letter: A unified accelerated maximum likelihood technique for widefield,confocal, and super‐resolution 4Pi microscopy

We propose an algorithmic technique for accelerating maximum likelihood (ML) algorithm for image reconstruction in fluorescence microscopy. This is made possible by integrating Biggs–Andrews (BA) method with ML approach. The results on widefield, confocal, and super‐resolution 4Pi microscopy reveal substantial improvement in the speed of 3D image reconstruction (the number of iterations has reduced by approximately one‐half). Moreover, the quality of reconstruction obtained using accelerated ML closely resembles with nonaccelerated ML method. The proposed technique is a step closer to realize real‐time reconstruction in 3D fluorescence microscopy. Microsc. Res. Tech. 78:331–335, 2015. © 2015 Wiley Periodicals, Inc.     

The influence of the regularization parameter and the first estimate on the performance of Tikhonov regularized non-linear image restoration algorithms

This paper reports studies on the influence of the regularization parameter and the first estimate on the performance of iterative image restoration algorithms. We discuss regularization parameter estimation methods that have been developed for the linear Tikhonov–Miller filter to restore images distorted by additive Gaussian noise. We have performed experiments on synthetic data to show that these methods can be used to determine the regularization parameter of non-linear iterative image restoration algorithms, which we use to restore images contaminated by Poisson noise. We conclude that the generalized cross-validation method is an efficient method to determine a value of the regularization parameter close to the optimal value. We have also derived a method to estimate the regularization parameter of a Tikhonov regularized version of the Richardson–Lucy algorithm.   These iterative image restoration algorithms need a first estimate to start their iteration. An obvious and frequently used choice for the first estimate is the acquired image. However, the restoration algorithm could be sensitive to the noise present in this image, which may hamper the convergence of the algorithm. We have therefore compared various choices of first estimates and tested the convergence of various iterative restoration algorithms. We found that most algorithms converged for most choices, but that smoothed first estimates resulted in a faster convergence.     

基于DCT变换的自适应图像水印实现

提出了一种基于DCT变换的自适应水印嵌入和检测方法。这种方法可以根据分块图像矩阵的特征,利用DCT变换的特点和自适应算法,在不同的分块区域中嵌入不同强度的水印,在图像的每个分块区域内都能实现水印强度和透明性的自适应调节。在检测水印时,还采用了一种有四级优先级的图像恢复方法,可以更好地恢复水印图像。利用这种方法成功地进行了水印的嵌入与检测,并对水印图像进行了量化攻击、JPEG压缩攻击、高斯噪声攻击和裁剪攻击检验。实验表明,这种方法可以在保证透明性的条件下,具备很好的鲁棒性,有较好的实用价值。     

一种基于小波域维纳滤波的图像复原算法

在图像复原算法中,单纯的空间域或者频域滤波算法简单易实现,但需要较多图像退化的先验知识。基于贝叶斯理论的迭代复原算法复原效果好,但耗时长。针对这一矛盾,利用小波变换的多分辨特性,对不同的小波系数特性采用不同的算法进行恢复,提出了一种基于小波域维纳滤波的图像复原算法。实验结果证明,所提方法在保证图像复原质量的同时相对提高了复原算法的效率,是一种有效的方法。     

An overview of state‐of‐the‐art image restoration in electron microscopy

In Life Science research, electron microscopy (EM) is an essential tool for morphological analysis at the subcellular level as it allows for visualization at nanometer resolution. However, electron micrographs contain image degradations such as noise and blur caused by electromagnetic interference, electron counting errors, magnetic lens imperfections, electron diffraction, etc. These imperfections in raw image quality are inevitable and hamper subsequent image analysis and visualization. In an effort to mitigate these artefacts, many electron microscopy image restoration algorithms have been proposed in the last years. Most of these methods rely on generic assumptions on the image or degradations and are therefore outperformed by advanced methods that are based on more accurate models. Ideally, a method will accurately model the specific degradations that fit the physical acquisition settings. In this overview paper, we discuss different electron microscopy image degradation solutions and demonstrate that dedicated artefact regularisation results in higher quality restoration and is applicable through recently developed probabilistic methods.     

一种Lucy-Richardson算法和小波变换结合的图像复原算法

Lucy-Richardson(LR)算法作为图像复原的经典算法之一,在进行图像复原时,对退化过程的先验知识要求少且复原效果好,然而由于算法采用迭代逼近的方法,故算法耗时长。针对这一缺点,用LR算法与小波变换结合的图像复原算法(即联合算法),分级对图像进行处理,在减小噪声干扰的同时提高算法的恢复效率。实验结果证明该方法在合理的范围内,以牺牲部分复原效果为代价却有效地提高了复原算法的效率。     

采用区域分割的变尺寸样本块高效图像修复

针对现有的基于样本块的图像修复算法采用全局搜索法确定最佳匹配样本块时效率低,且易出现错误匹配块的问题,分析了影响算法效率和质量的原因,提出了基于区域分割的变尺寸样本块高效图像修复算法。首先,采用双线性收缩方法获得尺寸是待修复图像0.02~0.25倍的收缩图像,并在收缩图像中分割出预选区域作为源区域,使用自适应窗口尺寸调整规则确定修复窗口大小;然后,在预选区域中搜索最佳匹配修复块对图像进行修复。收缩图像修复完毕后,对收缩图像中修复不完全的区域采用分割子图像的方法进行修复;最后,将子图像填回到原始图像的修复区域,循环运行直到修复完毕。实验结果表明,采用本文提出的修复算法,修复效率约为现有算法的5~100倍,且具有较好的修复质量。     

盲复原高斯模糊图像

经典的图像恢复算法设点扩展函数(PSF)是已知的,然而在许多情况下PSF难以确定,不得不在只知道成像系统部分信息甚至没有任何信息的情况下估计真实图像和PSF,这一过程称为图像盲复原.对于高斯模糊图像,它的PSF是很难被检测出来的,因此高斯模糊图像的盲复原一直是个棘手的问题.利用高斯点扩展函数的特性,初始估计PSF并对加...     

组合曝光的计算成像系统及其复原

传统光学成像方式在低照度条件下拍摄高速运动物体时,通过积分时间的配置难以平衡能量获取和高速运动模糊之间的矛盾。为了解决低照度条件下高速运动物体的清晰成像问题,本文提出一种基于组合曝光图像的计算成像方式,通过高帧频相机采集邻近两帧形成组合曝光图像对,基于两帧图像信息的互补,合理估算运动模糊点扩散函数,最终获取高信噪比的复原图像。试验结果表明,本文所述的计算成像方法能够良好地解决低照度条件下高速运动目标的成像模糊问题。通过本方法获得的复原图像相比原降质图像在细节纹理上有明显的改善,客观评价指标峰值信噪比(PSNR),结构相似指数(SSIM)相比原降质图像提升10%左右,整体性能效果优于现有非深度学习复原方法,图像清晰可靠,具有良好的主观视觉效果。     

联合自然梯度和AdamW算法的RSF图像分割模型

关键零件内部复杂结构的精密测量是高端制造领域攻克的难题。当采用工业CT技术实现对象内部结构精密测量时，面临目标图像灰度不均匀性、边缘模糊、伪影等问题。有鉴于此，本文研究了局部能量最小化模型(RSF)的图像分割方法，引入自然梯度和AdamW算法分别提高了RSF模型的收敛速度和参数自适应性。首先，在统计流形上计算自然梯度，提高梯度下降效率和RSF模型收敛速度；其次，采用AdamW算法实现RSF模型的高斯核函数尺度大小自适应控制。与经典RSF模型相比，改进后的RSF模型迭代次数减少了1 353次，迭代次数降低约76.79%,迭代时间减少约43.61%,测针球面半径和航空燃油喷嘴圆柱直径测量误差均较小，既保持了原模型亚像素分割精度，又大幅提高了模型收敛速度和鲁棒性。     

基于降质图像-光流场复原的智能轮椅测速

提出一种运动图像去模糊复原和基于仿射运动模型的光流场去抖动方法,以提高智能轮椅中光流里程计测速方法的精度。当轮椅线速度或角速度较大时,导致机载相机成像产生显著的运动模糊;且轮椅机器人的机械抖动也易产生光流场的偏差,进而影响速度估计的精度。针对于此,首先利用一种基于自适应模糊核的运动图像去模糊方法实现图像复原,以改善视频帧质量;其次,针对智能轮椅在行进中的机械抖动,利用随机抽样一致(RANSAC)排异后的光流场,在卡尔曼滤波框架下估计同名像点的仿射运动模型参数,进而实现光流补偿。实验结果表明所提方法能够提升基于光流场的智能轮椅视觉测速精度。     

Turbidity-adaptive underwater image enhancement method using image fusion

Clear, correct imaging is a prerequisite for underwater operations. In real freshwater environment including rivers and lakes, the water bodies are usually turbid and dynamic, which brings extra troubles to quality of imaging due to color deviation and suspended particulate. Most of the existing underwater imaging methods focus on relatively clear underwater environment, it is uncertain that if those methods can work well in turbid and dynamic underwater environments. In this paper, we propose a turbidity-adaptive underwater image enhancement method. To deal with attenuation and scattering of varying degree, the turbidity is detected by the histogram of images. Based on the detection result, different image enhancement strategies are designed to deal with the problem of color deviation and blurring. The proposed method is verified by an underwater image dataset captured in real underwater environment. The result is evaluated by image metrics including structure similarity index measure, underwater color image quality evaluation metric, and speeded-up robust features. Test results exhibit that the method can correct the color deviation and improve the quality of underwater images.     

H-S波前传感器在大尺寸测量中的应用研究

针对目前激光大尺寸测量系统中的空气扰动问题，提出一种采用波前探测和图像恢复技术相结合的解决方法。该方法采用Hartman—Shack波前探测器，直接应用Zemike多项式来波前重构，然后运用图像解卷积运算进行图像恢复，以减小和消除测量激光束因大气扰动而产生的抖动、变形，从而提高测量精度。     

自适应权值滤波消除图像椒盐噪声的方法

提出了一种自适应权值滤波算法。该方法利用邻域差分法(ROAD)判定滤波中心点的有效性,若判定该点为噪声,则将邻域像素分别赋予不同权值对该点像素重构,邻域像素权重系数的分配随该点有效程度的增加而增大。一方面抑制了图像噪声,另一方面保留了图像细节。实验证明该方法在滤除椒盐噪声时,无论从视觉角度还是数据恢复角度都比常规滤波方法效果有明显改善。当图像中含有35%的椒盐噪声时,利用该方法恢复数据效果比传统中值滤波提高10 dB。     

TDICCD相机平台振动所致的MTF空间移变降质

卫星振动不仅会引起TDI(Time Delay and Integration)CCD相机像元采样的不规则性,还会引起像元弥散斑空间分布的不一致性,导致图像传递函数(Modulation Transfer Function,MTF)产生空间变化,从而制约高分辨率图像振动复原质量。本文从卫星振动对TDICCD相机推扫成像采样过程的作用机理出发,结合理论推导与仿真研究,得到了平台振动引起的图像空间移变降质及表征方法。然后在振动复原模型的基础上,推导得出MTF空间移变降质对复原处理误差的影响,并基于实际卫星图像开展了复原实验。实验结果表明:当振动所致图像MTF空间移变降质小于15%时,复原处理后的图像与理想图像的结构相似度优于0.95,而且处理误差所导致的图像失真不影响判读质量。     

基于超分辨图像复原的显微圆孔孔径测量方法

为提高圆孔的光学显微测量准确性,研究了基于超分辨图像复原的显微圆孔孔径测量方法。该方法通过超分辨图像复原处理圆孔显微图像,提高了传统光学显微系统对圆孔成像的分辨率,确定了以超分辨复原图像灰度值为0.399作为圆孔物理边缘判据,实现对圆孔边缘的准确探测。理论分析表明该方法可准确测量微米级及以上直径圆孔。核孔膜孔径测量实验中,由二值化图像得到孔径测量结果为6.35μm(测量不确定度为0.08μm),与扫描电镜测量结果6.268μm(测量不确定度为0.083μm)相符,测量误差仅0.08μm。该技术有助于实现对圆孔形状的快速、准确在线测量。     

基于梯度先验的水下图像恢复

由于水体对光线的吸收和散射作用,水下图像呈现出强衰减、高噪声和色彩畸变等问题,难以满足视觉观察和图像分析 的需求。 针对这一问题,提出了一种基于梯度先验的水下图像恢复方法,用以提高水下图像质量。 首先,根据水下成像特性,建 立水下图像梯度先验,水下图像中目标反射信息(水下清晰图层)的梯度大于散射噪声信息(水下噪声图层);其次,根据水下成 像模型,对上述梯度先验进行表征建模;最后,建立水下图像的梯度分布优化函数,采用半二次优化方法分离出目标反信息,作 为水下图像恢复结果。 以 UEIB 和 RUIE 数据集为实验样本,与近年来所提出的 5 种水下图像处理方法进行比较,方法在定性 和定量两类评价中均获得了出色的处理结果,峰值信噪比(PSNR)、结构相似性(SSIM)和水下图像质量评价指标(UIQM)评价 指标分别达到 20. 066 5、0. 696 1 和 3. 902 9,均优于对比方法。 因此,该方法能够有效地抑制水下图像噪声,提高水下图像的信 噪比、清晰度和对比度。