高速运动目标的运动模糊图像复原研究

对单一图像进行运动模糊复原,存在模糊点扩散函数（PSF）难以估计以及图像反卷积的病态性问题。利用多个PSF具有联合可逆性的特点,针对运动目标观测,提出采用参数相同的多个成像设备共同对同一视场进行拍摄,来获取背景相同、曝光时间不同、目标模糊程度不同的观测图像;然后利用同一设备获取的序列图像进行目标的模糊PSF估计;并根据目标背景的运动模糊叠加特征,分别从观测图像中提取出完整的模糊目标图像;最后,对这些具有不同PSF的同一目标图像进行空间域迭代复原算式的联立求解。实验表明:该方法设计的目标获取装置对硬件条件要求较低,获取的图像更便于采用多点扩散函数联合进行图像复原,复原效果良好。     

Blind deblurring of spiral CT images

To discriminate fine anatomical features in the inner ear, it has been desirable that spiral computed tomography (CT) may perform beyond their current resolution limits with the aid of digital image processing techniques. In this paper, we develop a blind deblurring approach to enhance image resolution retrospectively without complete knowledge of the underlying point spread function (PSF). An oblique CT image can be approximated as the convolution of an isotropic Gaussian PSF and the actual cross section. Practically, the parameter of the PSF is often unavailable. Hence, estimation of the parameter for the underlying PSF is crucially important for blind image deblurring. Based on the iterative deblurring theory, we formulate an edge-to-noise ratio (ENR) to characterize the image quality change due to deblurring. Our blind deblurring algorithm estimates the parameter of the PSF by maximizing the ENR, and deblurs images. In the phantom studies, the blind deblurring algorithm reduces image blurring by about 24%, according to our blurring residual measure. Also, the blind deblurring algorithm works well in patient studies. After fully automatic blind deblurring, the conspicuity of the submillimeter features of the cochlea is substantially improved.     

Automated 3-D reconstruction of the surface of live early-stage amphibian embryos

Although three-dimensional (3-D) reconstructions of the surfaces of live embyos are vital to understanding embryo development, morphogenetic tissue movements and other factors have prevented the automation of this task. Here, we report an integrated set of software algorithms that overcome these challenges, making it possible to completely automate the reconstruction of embryo surfaces and other textured surfaces from multiview images. The process involves: 1) building accurate point correspondences using a robust deformable template block matching algorithm; 2) removing outliers using fundamental matrix calculations in conjunction with a RANSAC algorithm; 3) generating 3-D point clouds using a bundle adjustment algorithm that includes camera position and distortion corrections; 4) meshing the point clouds into triangulated surfaces using a Tight Cocone algorithm that produces water tight models; 5) refining surfaces using midpoint insertion and Laplacian smoothing algorithms; and 6) repeating these steps until a measure of convergence G, the rms difference between successive reconstructions, is below a specified threshold. Reconstructions were made of 2.2-mm diameter, neurulation-stage axolotl (amphibian) embryos using 44 multiview images collected with a robotic microscope. A typical final model (sixth iteration) contained 3787 points and 7562 triangles and had an error measure of G = 5.9 microm.     

气动光学效应红外序列退化图像优化复原算法

从时间序列退化图像中依次连续取两帧图像来估计湍流瞬态点扩展函数,将约束优化原理应用在气动光学效应退化图像的复原过程中。针对气动光学效应湍流点扩展函数复杂多峰、随机多变等特性,在点扩展函数的估计过程中,采用保凸峰等优化策略,将点扩展函数离散值的计算转化为基于松弛迭代的最优化估计,通过极小化准则函数估计点扩展函数值,进而恢复退化图像。实验结果表明,本文方法复原效果好,速度较快。     

Variational Image Restoration and Decomposition in Shearlet Smoothness Spaces

We present the shearlet-based variational model for image restoration and decomposition.The new model can be seen as generalizations of Daubechies-Teschke's model.By using regularization term in shearlets smoothness spaces,and writing the problem in a shearlet framework,we obtain elegant shearlet shrinkage schemes.Furthermore,the model allows us to incorporate general bounded linear blur operators into the problem.The experiments on denoising,deblurring and decomposition of images show that our algorithm is very efficient.     

Multi-frame real image restoration based on double loops with alternative maximum likelihood estimation

A multi-frame blind restoration algorithm based on alternative maximum likelihood estimation using double loops is proposed to restore object images. An anisotropic regularization term is incorporated into the integral likelihood function to avoid over-smoothing of details of the restored images and improve the stability of image restoration. In order to make full use of the data relationship in the observed multi-frame images which contain the same objects with different point spread functions (PSF) in a short sequence, an iterative strategy based on double loops is employed in estimating the unknown different PSFs of the observed images. Experiments on synthetic and real turbulence-degraded images illustrate that the proposed algorithm is effective in restoring real turbulence-degraded images by using very few frames.     

An L0 regularized cartoon-texture decomposition model for restoring images corrupted by blur and impulse noise

In this paper, we address the image restoration case that includes both blurring and impulse noise. To recover an image with abundant features, we propose an L0 regularized cartoon-texture model for the simultaneous deblurring and impulse noise removal problem. We propose an L0 regularized framelet-based sparse representation and L0 regularized discrete cosine transform (DCT)-based sparse approximation to model the cartoon and texture of images, respectively. Unlike other cartoon-texture decomposition based-restoration approaches, our method does not depend on local features but globally controls the important non-zero components of the cartoon and texture in the framelet and DCT domain. Furthermore, we develop an alternating half-quadratic splitting method to solve the proposed L0 regularized cartoon-texture deblurring and impulse noise removal model (L0_RCTDINR) by introducing an alternating algorithm into the half-quadratic method. Experiments show the effectiveness of L0_RCTDINR on deblurring and impulse noise removal compared with existing state-of-the-art methods.     

一种面向目标区域的快速去模糊算法

本文针对监视视频图像中快速移动目标的运动模糊问题,提出一种面向目标区域的快速去运动模糊算法。本文新算法的实现过程可归纳为三步:首先,通过分析图像运动模糊的离散模型,得出去模糊逆运算迭代公式;其次,针对本文实际研究对象,对相邻帧进行差值计算,利用连续两幅帧差图像提取目标运动矢量以及获取模糊目标所在区域;最后,依据图像去运动模糊的离散迭代模型,对模糊目标区域代入运动矢量进行逆运算,实现对目标区域的去运动模糊处理。仿真试验表明,该方法不仅从空间上缩小了去模糊逆运算范围,处理速度快,而且该方法降低了背景中大片随机噪声对去模糊效果的影响,去运动模糊质量好。?      

Point-source localization in blurred images by a frequency-domaineigenvector-based method

We address the problem of resolving and localizing blurred point sources in intensity images. Telescopic star-field images blurred by atmospheric turbulence or optical aberrations are typical examples of this class of images, a new approach to image restoration is introduced, which is a generalization of 2-D sensor array processing techniques originating from the field of direction of arrival estimation (DOA). It is shown that in the frequency domain, blurred point source images can be modeled with a structure analogous to the response of linear sensor arrays to coherent signal sources. Thus, the problem may be cast into the form of DOA estimation, and eigenvector based subspace decomposition algorithms, such as MUSIC, may be adapted to search for these point sources. For deterministic point images the signal subspace is degenerate, with rank one, so rank enhancement techniques are required before MUSIC or related algorithms may be used. The presence of blur prohibits the use of existing rank enhancement methods. A generalized array smoothing method is introduced for rank enhancement in the presence of blur, and to regularize the ill posed nature of the image restoration. The new algorithm achieves inter-pixel super-resolution and is computationally efficient. Examples of star image deblurring using the algorithm are presented.     

利用拉东变换估计全景图像点扩散函数

针对全景图像点扩散函数(PSF)空间移变的特点, 提出一种利用拉东(Radon)变换估计全景图像局部区域的PSF算法。算法首先分析全景成像 原理和PSF拉东变换与图像中模糊直线响应的关系; 然后利用有效的采样方法得到孤立的采样边缘,并通过求导得到图像边缘的直线响应;最后 利用采样信息 复原出PSF并结合Lucy-Richardson(L-R)算法对图像进行复原。同时采用迭代最优 化的估计算法解决 由于采样边缘较少导致PSF估计的不准确问题。实验表明,本文算法可以有效地估 计出全景图像不同区域的PSF,有很好的工程使用价值。     

基于模糊算子的红外图像去模糊研究

针对镜头抖动,目标移动等因素引起的图像运动模糊问题,本文提出了一种基于模糊算子的红外图像去模糊算法,使用深度自编码网络对数据集中的模糊算子进行编码,通过编码后的模糊算子去逼近一个未知的模糊算子并搜索对应的清晰图像,从而实现真实场景下红外图像去模糊,弥补了现有基于深度学习的图像去模糊模型在跨域应用时对真实场景下运动模糊图像去模糊效果较差的不足。在红外图像上的实验结果表明,相比于其他去模糊算法,本文提出的去模糊算法取得了更高的性能指标,恢复出的图像有着清晰的边缘轮廓和局部细节,显著提升了红外图像的清晰度。     

Distributed representation of geometrically correlated images with compressed linear measurements

This paper addresses the problem of distributed coding of images whose correlation is driven by the motion of objects or the camera positioning. It concentrates on the problem where images are encoded with compressed linear measurements. We propose a geometry-based correlation model that describes the common information in pairs of images. We assume that the constitutive components of natural images can be captured by visual features that undergo local transformations (e.g., translation) in different images. We first identify prominent visual features by computing a sparse approximation of a reference image with a dictionary of geometric basis functions. We then pose a regularized optimization problem in order to estimate the corresponding features in correlated images that are given by quantized linear measurements. The correlation model is thus given by the relative geometric transformations between corresponding features. We then propose an efficient joint decoding algorithm that reconstructs the compressed images such that they are consistent with both the quantized measurements and the correlation model. Experimental results show that the proposed algorithm effectively estimates the correlation between images in multiview data sets. In addition, the proposed algorithm provides effective decoding performance that advantageously compares to independent coding solutions and state-of-the-art distributed coding schemes based on disparity learning.     

航天湍流退化图像的极大似然估计规整化复原算法

为了从有噪的湍流退化图像中有效地恢复出目标图像，提出了一种基于极大似然估计准则的规整化复原算法．根据图像随机场模型建立了有关多帧图像数据的对数似然函数，同时为了平滑噪声和保护图像边缘以及避免无价值的解，将一些合理的惩罚项和辅助平滑项融合到该对数似然函数中．推导出了湍流点扩展函数和目标图像的交替迭代求解公式，通过迭代方式可将点扩展函数和目标图像同时估计出来，给出了算法的并行处理方案．在微机上对强噪声条件下的湍流退化图像进行了恢复实验，实验结果表明本算法具有较强的抗噪能力和实用价值．     

PDE-based enhancement of color images in RGB space

A novel method for color image enhancement is proposed as an extension of the scalar-diffusion-shock-filter coupling model, where noisy and blurred images are denoised and sharpened. The proposed model is based on using the single vectors of the gradient magnitude and the second derivatives as a manner to relate different color components of the image. This model can be viewed as a generalization of the Bettahar-Stambouli filter to multivalued images. The proposed algorithm is more efficient than the mentioned filter and some previous works at color images denoising and deblurring without creating false colors.     

Deblurring Using Regularized Locally Adaptive Kernel Regression

Kernel regression is an effective tool for a variety of image processing tasks such as denoising and interpolation . In this paper, we extend the use of kernel regression for deblurring applications. In some earlier examples in the literature, such nonparametric deblurring was suboptimally performed in two sequential steps, namely denoising followed by deblurring. In contrast, our optimal solution jointly denoises and deblurs images. The proposed algorithm takes advantage of an effective and novel image prior that generalizes some of the most popular regularization techniques in the literature. Experimental results demonstrate the effectiveness of our method.     

基于振铃约束的全变差正则化图像去模糊算法

当前去模糊方法只利用图像单一的稀疏特性作为先验信息,忽略了伪边缘(如振铃瑕疵)对模糊核估计的影响,导致其去模糊性能不佳.本文充分利用复杂结构图像的先验信息,设计了振铃约束下的全变差正则化图像去模糊算法.首先,利用多分辨率图像金字塔策略建立多层图像模型,通过对比模糊图像和潜在清晰图像来获得振铃先验信息.其次,将振铃正则约...     

Spiral CT image deblurring for cochlear implantation

Cochlear implantation is the standard treatment for profound hearing loss, Preimplantation and postimplantation spiral computed tomography (CT) is essential in several key clinical and research aspects. The maximum image resolution with commercial spiral CT scanners is insufficient to define clearly anatomical features and implant electrode positions in the inner ear, In this paper, the authors develop an expectation maximization (EM)-like iterative deblurring algorithm to achieve spiral CT image super-resolution for cochlear implantation, assuming a spatially invariant linear spiral CT system with a three-dimensional (3-D) separable Gaussian point spread function (PSF). The authors experimentally validate the 3-D Gaussian blurring model via phantom measurement and profile fitting. The imaging process is further expressed as convolution of an isotropic 3-D Gaussian PSF and a blurred underlying volumetric image. Under practical conditions, an oblique reconstructed section is approximated as convolution of an isotropic two dimensional (2-D) Gaussian PSF and the corresponding actual cross section. The spiral CT image deblurring algorithm is formulated with sieve and resolution kernels for suppressing noise and edge artifacts. A typical cochlear cross section is used for evaluation, demonstrating a resolution gain up to 30%-40% according to the correlation criterion. Physical phantoms, preimplantation and postimplantation patients are reconstructed into volumes of 0.1-mm cubic voxels. The patient images are digitally unwrapped along the central axis of the cochlea and the implanted electrode array respectively, then oblique sections orthogonal to the central axis formed. After deblurring, representation of structural features is substantially improved in all the cases     

Systematic analysis of the decoding delay in multiview video

We present a framework for the analysis of the decoding delay in multiview video coding (MVC). We show that in real-time applications, an accurate estimation of the decoding delay is essential to achieve a minimum communication latency. As opposed to single-view codecs, the complexity of the multiview prediction structure and the parallel decoding of several views requires a systematic analysis of this decoding delay, which we solve using graph theory and a model of the decoder hardware architecture. Our framework assumes a decoder implementation in general purpose multi-core processors with multi-threading capabilities. For this hardware model, we show that frame processing times depend on the computational load of the decoder and we provide an iterative algorithm to compute jointly frame processing times and decoding delay. Finally, we show that decoding delay analysis can be applied to design decoders with the objective of minimizing the communication latency of the MVC system.     

张量低秩约束下的多帧图像去模糊

矩阵低秩估计模型在图像处理任务中有着广泛地运用.针对图像去模糊,利用矩阵低秩先验能保留图像的重要边缘信息从而实现去模糊.而对于多帧图像去模糊,基于矩阵的低秩模型并未充分考虑多帧图像间的时序和空间关系.针对该问题,我们提出基于三维张量低秩先验的多帧视频图像盲去模糊模型.在模型中,首先将多帧连续图像按时序维堆叠成张量,显式...     

Two-dimensional microwave imaging approach based on a geneticalgorithm

A multi-illumination multiview approach to microwave imaging is proposed, which is based on a genetic algorithm (GA). The inverse problem is recast in an optimization problem, solved in the framework of the Born approximation. The formulation of the approach is described and images of reconstructed circular (multilayer) cylinders are reported