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Multichannel blind iterative image restoration 总被引:3,自引:0,他引:3
Blind image deconvolution is required in many applications of microscopy imaging, remote sensing, and astronomical imaging. Unfortunately, in a single-channel framework, serious conceptual and numerical problems are often encountered. An eigenvector-based method (EVAM) has been proposed for a multichannel framework which determines perfectly convolution masks in a noise-free environment if channel disparity, called co-primeness, is satisfied (see Harikumar, G. and Bresler, Y., ibid., vol.8, no.2, p.202-19, 1999; Proc. ICIP 96, vol.3, p.97-100, 1996). We propose a novel iterative algorithm based on recent anisotropic denoising techniques of total variation and a Mumford-Shah functional with the EVAM restoration condition included. A linearization scheme of half-quadratic regularization together with a cell-centered finite difference discretization scheme is used in the algorithm and provides a unified approach to the solution of total variation or Mumford-Shah. The algorithm performs well even on very noisy images and does not require an exact estimation of mask orders. We demonstrate the capabilities of the algorithm on synthetic data. Finally, the algorithm is applied to defocused images taken with a digital camera and to data from astronomical ground-based observations of the Sun. 相似文献
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A VQ-based blind image restoration algorithm 总被引:5,自引:0,他引:5
Learning-based algorithms for image restoration and blind image restoration are proposed. Such algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighbor approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms. 相似文献
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传统的模糊图像盲复原算法的纹理复杂度高,降低了模糊图像的质量。为此,文中设计了基于纹理复杂度的含噪模糊图像盲复原算法。首先,分析含噪模糊图像的噪声特点,在保留原有图像信息的前提下计算垂直方向和水平方向的扩散系数,得到ADF在含噪模糊图像上的表达,完成含噪模糊图像的去噪预处理。通过建立含噪模糊图像的数学模型描述其退化过程,继而构建含噪模糊图像降质模型。最后,在纹理复杂度分析的基础上,完成奇异值分解检测和alpha通道的计算,通过合成操作实现含噪模糊图像的盲复原算法的设计。实验结果表明,相比于传统盲复原算法,在所提算法下,图像的纹理复杂度低,图像质量得以提升,且复原结果的误差较小,算法整体有效性较高。 相似文献
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Existing eigenstructure-based direct multichannel blind image restoration techniques include nullspace-based and direct deconvolver estimation techniques. The nullspace-based approach can be formulated as an optimization problem. We show that this formulation implies a new subspace-based approach that uses matrix operations. This new approach has the same advantages as the nullspace-based one but requires less computational complexity. Under some mild conditions, its complexity is equal to that of the FFT. Furthermore, the relation among the nullspace-based approach, the direct deconvolver estimation and the new subspace-based approach is studied. 相似文献
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Shao Wen-Ze Wang Feng Huang Li-Li 《Multidimensional Systems and Signal Processing》2019,30(2):857-883
Multidimensional Systems and Signal Processing - In this paper, a fast blind deconvolution approach is proposed for image deblurring by modifying a recent well-known natural image model, i.e., the... 相似文献
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Chow T.W.S. Xiao-Dong Li Cho S.-Y. 《Vision, Image and Signal Processing, IEE Proceedings -》2000,147(1):23-28
In the paper, the idea of space-adaptive regularisation is introduced into the nonnegativity and support constraints recursive inverse filtering (NAS-RIF) algorithm for blind image restoration. A newly developed cost function is obtained by adding a space-adaptive regularisation term to the cost function of the NAS-RIF algorithm to prevent noise amplification and to reduce ringing artifacts. Compared to the original NAS-RIF algorithm, the improved NAS-RIF algorithm is particularly effective under low SNR conditions. Also, the experimental results show that the convergence characteristic of the improved NAS-RIF algorithm is much more stable and the converged solution is capable of providing an excellent estimate of the original image 相似文献
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针对目前大多数深度相机采集到的深度图像中含有大量噪点以及大面积的空洞问题,提出一种基于图像融合的深度图像修复算法。采用改进分水岭算法提取彩色图像中的边缘信息,基于KD树近邻算法依据深度图像的梯度信息提取分类信息,将彩色图像的边缘信息与深度图像像素点的分类信息相结合,得到精确地图像分类结果,再对融合后的每一类进行最小二乘法算法拟合空洞,修复深度图像中出现的大面积空洞问题。实验结果表明,该方法在对物体边缘处小面积空洞进行较为准确地修复的同时,能够对深度图像中存在的大面积空洞问题进行有效修复。 相似文献
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Most deep learning (DL)-based image restoration methods have exploited excellent performance by learning a non-linear mapping function from low quality images to high quality images. However, two major problems restrict the development of the image restoration methods. First, most existing methods based on fixed degradation suffer from significant performance drop when facing the unknown degradation, because of the huge gap between the fixed degradation and the unknown degradation. Second, the unknown-degradation estimation may lead to restoration task failure due to uncertain estimation errors. To handle the unknown degradation in the real application, we introduce a degradation representation network for single image blind restoration (DRN). Different from the methods of estimating pixel space, we use an encoder network to learn abstract representations for estimating different degradation kernels in the representation space. Furthermore, a degradation perception module with flexible adaptability to different degradation kernels is used to restore more structural details. In our experiments, we compare our DRN with several state-of-the-art methods for two image restoration tasks, including image super-resolution (SR) and image denoising. Quantitative results show that our degradation representation network is accurate and efficient for single image restoration. 相似文献
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Lun D.P.K. Chan T.C.L. Tai-Chiu Hsung Feng D.D. Yuk-Hee Chan 《IEEE transactions on image processing》2004,13(2):188-200
Restoring an image from its convolution with an unknown blur function is a well-known ill-posed problem in image processing. Many approaches have been proposed to solve the problem and they have shown to have good performance in identifying the blur function and restoring the original image. However, in actual implementation, various problems incurred due to the large data size and long computational time of these approaches are undesirable even with the current computing machines. In this paper, an efficient algorithm is proposed for blind image restoration based on the discrete periodic Radon transform (DPRT). With DPRT, the original two-dimensional blind image restoration problem is converted into one-dimensional ones, which greatly reduces the memory size and computational time required. Experimental results show that the resulting approach is faster in almost an order of magnitude as compared with the traditional approach, while the quality of the restored image is similar. 相似文献
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Although the use of blind deconvolution of image restoration is a widely known concept, little literatures have discussed in detail its application in the problem of restoration of underwater range-gated laser images. With the knowledge of the point spread function (PSF) and modulation transfer function (MTF) of water,underwater images can be better restored or enhanced. We first review image degradation process and Wells' small angle approximation theory, and then provide an image enhancement method for our underwater laser imaging system by blind deconvolution method based on small angle approximation. We also introduce a modified normalized mean square error (NMSE) method to validate the convergence of the blind deconvolution algorithm which is applied in our approach. The results of different initial guess of blind deconvolution are compared and discussed. Moreover, restoration results are obtained and discussed by intentionally changing the MTF parameters and using non-model-based PSF as the initial guess. 相似文献
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在毫米波的图像恢复中,L-R算法是一种简单而有效的非线性方法,但当噪声不可忽略时,L-R算法难以获得较好的复原结果。针对毫米波图像数据量少和图像分辨率低的特点,提出基于改进自蛇模型和L-R算法毫米波图像恢复方法,以局部方差构造自蛇模型的边缘停止函数,其改进自蛇模型在消除噪声的同时更能够保留图像中的边缘和细节特征,然后使用L-R算法进行图像恢复,这种改进算法通过使用基于改进自蛇模型去噪能有效地减少噪声对L-R算法的影响。实验结果表明:在信噪比和相关度方面本文算法提高了L-R算法的性能,可用于含噪声的图像复原。 相似文献
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We present a novel blind deconvolution technique for the restoration of linearly degraded images without explicit knowledge of either the original image or the point spread function. The technique applies to situations in which the scene consists of a finite support object against a uniformly black, grey, or white background. This occurs in certain types of astronomical imaging, medical imaging, and one-dimensional (1-D) gamma ray spectra processing, among others. The only information required are the nonnegativity of the true image and the support size of the original object. The restoration procedure involves recursive filtering of the blurred image to minimize a convex cost function. We prove convexity of the cost function, establish sufficient conditions to guarantee a unique solution, and examine the performance of the technique in the presence of noise. The new approach is experimentally shown to be more reliable and to have faster convergence than existing nonparametric finite support blind deconvolution methods. For situations in which the exact object support is unknown, we propose a novel support-finding algorithm 相似文献
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一种改进的总变分正则化图像盲复原方法 总被引:1,自引:0,他引:1
传统的图像复原算法多数采用最小均方误差作为图像复原效果的评价标准,很少考虑人的视觉感受。本文在总变分盲复原算法的基础上结合Weber定律和正则化方法,运用不同的迭代表达式:在模糊辨识阶段,采用总变分正则化算法进行辨识;在图像复原阶段,采用Weber定律和正则化方法相结合。正则化的选择充分考虑图像的细节保持和边缘增强。实验结果采用基于人眼视觉感受的图像评价标准来验证。实验证明该算法在未知点扩散函数的情况下不但能有效的抑制噪声和消除纹波现象,而且还能有较好的视觉效果。 相似文献
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选用TMS320DM642作为处理器开发了图像退化仿真和复原系统.可以实现图像采集、退化仿真、逆滤波和维纳滤波复原.在软件设计中采用多种优化措施,如合理分配内存,二维数据片外存储,功能强大的Image Lib库函数调用,DMA方式实现片内、外存储器之间的数据交换;行列算法实现二维FFT;在复原时用定点乘法代替耗时的浮点除法运算;通过合理定标确保二维FFT和IFFT过程不产生溢出;模板平滑取代卷积加快图像模糊运算;使用内联函数、循环展开、软件流水、编译选项等,这些措施的应用,大大减小了运算量,提高了处理速度.对256×256图像进行的模糊、逆滤波和维纳滤波复原运算,帧率达到10帧/s. 相似文献
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针对传统变分模型在修复图像时易产生“阶梯效应”与细节模糊等问题,提出了一种基于图像分解的自适应二阶总广义变分和分数阶变分的图像修复算法。首先将待修复的目标图像分解为卡通部分与纹理部分,其中卡通对应目标图像的低、中频部分,因此利用抑制“阶梯效应”较好的二阶总广义变分模型对其进行修复;纹理对应其高频部分,因此利用对细节部分有增强效果的分数阶变分模型对其进行修复。由于文中所提到的修复模型均与线性鞍点结构下求取最优值的模型类似,因此在算法上均采用基于预解式的原始对偶算法对新模型进行求解。另外,为了取得更好的修复效果,文中设计了一个边缘指示算子来自适应地控制新模型的扩散,以更好地保护修复图像的边缘细节。实验结果表明:相比传统的TV、TGV修复模型,新模型的修复效果在主观视觉上显得更加自然,且在峰值信噪比与相关系数等客观评价指标上均有提高。 相似文献