Adaptive Bregmanized total variation model for mixed noise removal

To solve the problem of the staircase effect and removing mixed noise, including Gaussian and salt and pepper noise, which can occur during image formation and transmission, a total variation-based denoising model using an edge detection function is proposed. Moreover, an improved split Bregman iteration method is used to solve the nonlinear problem. The results of comparative experiments demonstrate that the proposed model removes Gaussian and salt and pepper noise while maintaining detailed features.     

基于各向异性规整化的总变分盲复原算法研究

针对大气湍流退化图像复原问题,提出了一种基于各向异性和非线性规整化的总变分盲复原新算法,该算法主要结合图像和湍流点扩展函数的一些性质采用基于各向异性的空间自适应规整化处理,建立了具有非线性和空间各向异性的规整化函数,使其在恢复目标图像和估计点扩展函数时能自适应地进行梯度平滑。最后,通过交替最小化方案来极小化代价函数和通过定点迭代策略将非线性方程进行线性化处理,快速地估计点扩展函数和恢复图像。在微机上对数字模拟和实际退化图像进行了一系列恢复实验,验证了算法的有效性和稳健性。     

基于各向异性总变分的运动模糊盲复原方法

针对目前运动模糊图像盲复原算法对图像边缘中 拐角结构复原不佳这一问题,提出了一种以各向异性总变分为图像和模糊核正则项的遥感图 像盲 复原方法。为便于进行数值计算,采用交叉算法和分裂布雷格曼迭代导出了本文提出的盲复 原方法的迭 代公式。实验结果表明,与基于同向异性总变分的盲复原方法以及基于小波框架的盲复原方 法相 比,本文方法不仅能估算出较精确的点扩散函数(PSF,point sp read function),有效地 去除图像的模糊效应,而且对图像边缘结构特别是拐角边缘结构的增强有着独特的优势。     

一种复合局部与非局部梯度的图像去噪变分模型

局部变分有效地增强图像的轮廓信息,但不可避免地模糊图像的细节并在平滑区域产生阶梯效应。非局部变分能有效重构图像的纹理信息,但同时会破坏图像的结构轮廓信息。考虑到局部与非局部变分的互补性,提出了一种基于图像局部梯度与非局部梯度的复合变分模型,并通过Bregman交替迭代极小化图像的局部梯度与非局部梯度的L1范数,使去噪后的图像在去除噪声的同时更好地保留图像的结构与细节信息。对比实验证明,提出的复合变分模型有效地利用了图像的局部变分与非局部变分的优点,在图像评价的主客观方面都表现出了更好的性能。     

Perceptual quality evaluation for image defocus deblurring

Blur is one of the most common distortion types in image acquisition. Image deblurring has been widely studied as an effective technique to improve the quality of blurred images. However, little work has been done to the perceptual evaluation of image deblurring algorithms and deblurred images. In this paper, we conduct both subjective and objective studies of image defocus deblurring. A defocus deblurred image database (DDID) is first built using state-of-the-art image defocus deblurring algorithms, and subjective test is carried out to collect the human ratings of the images. Then the performances of the deblurring algorithms are evaluated based on the subjective scores. With the observation that the existing image quality metrics are limited in predicting the quality of defocus deblurred images, a quality enhancement module is proposed based on Gray Level Co-occurrence Matrix (GLCM), which is mainly used to measure the loss of texture naturalness caused by deblurring. Experimental results based on the DDID database demonstrate the effectiveness of the proposed method.     

Spatially variant defocus blur map estimation and deblurring from a single image

In this paper, we propose a single image deblurring algorithm to remove spatially variant defocus blur based on the estimated blur map. Firstly, we estimate the blur map from a single image by utilizing the edge information and K nearest neighbors (KNN) matting interpolation. Secondly, the local kernels are derived by segmenting the blur map according to the blur amount of local regions and image contours. Thirdly, we adopt a BM3D-based non-blind deconvolution algorithm to restore the latent image. Finally, ringing artifacts and noise are detected and removed, to obtain a high quality in-focus image. Experimental results on real defocus blurred images demonstrate that our proposed algorithm outperforms some state-of-the-art approaches.     

Fast total variation deconvolution for blurred image contaminated by Poisson noise

In this paper, we present a fast non-blind deconvolution method for restoring blurred images contaminated by Poisson noise. The problem is formulated by finding the minimizer of the negative logarithmic Poisson log-likelihood combined with the total variation (TV). To attack the challenging task, we adopt the well-known variable splitting and penalty technique to convert the problem into two easier sub-problems: one is a modified TV regularized deconvolution and the other is a simple convex optimization problem. Experimental results show that the proposed method runs very fast and the quality of the restored image is comparable with that of some state of the art methods.     

An edge fusion scheme for image denoising based on anisotropic diffusion models

In this paper, we propose an enhanced anisotropic diffusion model. The improved model can classify finely image information as smooth regions, edges, corners and isolated noises by characteristic parameters and gradient variance parameter. And for different image information the eigenvalues of diffusion tensor are designed to conduct adaptive diffusion. Moreover, an edge fusion scheme is posed to preserve edges after denoising by combing different denoising and edge detection methods. Firstly, different denoising methods are applied for noisy image to obtain denoised images, and the best method among them is selected as main method. Then edge images of denoised images are obtained by edge detection methods. Finally, by fusing edge images together more integrated edges can be achieved to replace edges of denoised image obtained by main method. The experimental results show the proposed model can denoise meanwhile preserve edges and corners, and the edge fusion scheme is accurate and effective.     

Fast optimization for multichannel total variation minimization with non-quadratic fidelity

Total variation (TV) has been proved very successful in image processing, and it has been combined with various non-quadratic fidelities for non-Gaussian noise removal. However, these models are hard to solve because TV is non-differentiable and nonlinear, and non-quadratic fidelity term is also nonlinear and even non-differentiable for some special cases. This prevents their widespread use in practical applications. Very recently, it was found that the augmented Lagrangian method is extremely efficient for this kind of models. However, only the single-channel case (e.g., gray images) is considered. In this paper, we propose a general computational framework based on augmented Lagrangian method for multichannel TV minimization with non-quadratic fidelity, and then show how to apply it to two special cases: L¹ and Kullback-Leibler (KL) fidelities, two common and important data terms for blurry images corrupted by impulsive noise or Poisson noise, respectively. For these typical fidelities, we show that the sub-problems either can be fast solved by FFT or have closed form solutions. The experiments demonstrate that our algorithm can fast restore high quality images.     

Kernel estimation from salient structure for robust motion deblurring

Blind image deblurring algorithms have been improving steadily in the past years. Most state-of-the-art algorithms, however, still cannot perform perfectly in challenging cases, especially in large blur setting. In this paper, we focus on how to estimate a good blur kernel from a single blurred image based on the image structure. We found that image details caused by blur could adversely affect the kernel estimation, especially when the blur kernel is large. One effective way to remove these details is to apply image denoising model based on the total variation (TV). First, we developed a novel method for computing image structures based on the TV model, such that the structures undermining the kernel estimation will be removed. Second, we applied a gradient selection method to mitigate the possible adverse effect of salient edges and improve the robustness of kernel estimation. Third, we proposed a novel kernel estimation method, which is capable of removing noise and preserving the continuity in the kernel. Finally, we developed an adaptive weighted spatial prior to preserve sharp edges in latent image restoration. Extensive experiments testify to the effectiveness of our method on various kinds of challenging examples.     

Total variation blind deconvolution employing split Bregman iteration

Blind image deconvolution is one of the most challenging problems in image processing. The total variation (TV) regularization approach can effectively recover edges of image. In this paper, we propose a new TV blind deconvolution algorithm by employing split Bregman iteration (called as TV-BDSB). Considering the operator splitting and penalty techniques, we present also a new splitting objective function. Then, we propose an extended split Bregman iteration to address the minimizing problems, the latent image and the blur kernel are estimated alternately. The TV-BDSB algorithm can greatly reduce the computational cost and improve remarkably the image quality. Experiments are conducted on both synthetic and real-life degradations. Comparisons are also made with some existing blind deconvolution methods. Experimental results indicate the advantages of the proposed algorithm.     

基于整体变分模型的图像修复算法仿真实现

为了提高受损数字图像修复质量,将整体变分模型用于图像修复。采用Matlab编程工具对修复过程进行了仿真实现,克服了直接求解偏微分方程的困难。发现随着迭代次数的增加,被修复区域接近原图质量,当受损区域较小且受损区域灰度梯度不大时,看不出明显的修复痕迹。仿真实验结果表明,该算法复原图像的视觉质量好,算法收敛快,特别适合于线状裂痕的图像修补。     

基于空-时加权全变差正则项的视频复原算法

该文改进空-时全变正则项,提出了基于空-时加权全变差的视频图像重建算法。通过空-时加权全变差正则项的引入,获得新的视频重建模型,并提出了基于分裂 Bregman迭代算法的模型快速求解方法。仿真和数值实验表明,该文算法能够有效地实现高斯白噪声背景下视频序列去模糊问题,而且能够较好地保持复原图像序列的边缘和细节信息,避免传统TV算法产生的过平滑而失去细节信息的缺点,获得更加自然和细节的复原图像。     

Oriented total variation l1/2 regularization

Total Variation (TV) is a widely used image restoration/decomposition model. It is observed that the classical TV l1 and TV l2 regularization, on the one hand, do not favor higher-gradient structures over lower-gradient details as expected for structure preserving image processing, and on the other hand, tend to reduce the horizontal and vertical gradients, and thus inevitably blur the oblique edges in images. In this paper, we address these two problems by defining Oriented Total Variation l1/2 (OTV l1/2). It is theoretically and experimentally demonstrated that applying l1/2 regularization to the directional derivatives of images leads to superior structure preservation. OTV l1/2 regularization can be applied to image denoising and video compression, and the experimental results verify that OTV l1/2 outperforms other similar models.     

基于L1-L2范数的正则项去噪模型的PCB图像去噪算法研究

电子行业常通过提取图像特征来对印刷电路板(Printed Circuit Board,PCB)进行缺陷识别。为了改善PCB图像的视觉效果,提升PCB无损检测的准确率,本文提出了一种基于L1-L2范数的正则项去噪模型的PCB图像去噪算法。首先采用非局部均值(Non Local Mean,NLM)滤波算法将提取的图像分解为结构和纹理两个部分,根据结构框架和纹理细节差异化的物理特性,分别使用Lasso回归算法和Ridge回归算法进行图像去噪,然后将Split Bregman迭代框架应用到去噪模型中,最后通过MATLAB软件平台对所提算法进行实验探究,并从视觉角度和去噪效果指标SNR、SSIM等多方面对算法进行评估。实验结果证明了基于L1-L2范数的正则项去噪模型的PCB图像去噪算法的有效性和可行性。     

一种基于L_1范数的非局部变分图像复原模型

针对L_2范数的非局部变分模型在迭代过程中未考虑图像局部梯度信息,模糊图像细节信息的缺点,提出了一种基于L_1范数的非局部变分模型。首先,对基于L_1范数的非局部变分模型的扩散性能进行了详细的分析。接着,将该模型应用于退化图像的复原中,并推导出该模型的Bregman交替迭代求解过程。最后,通过对比实验,证明本文提出的L_1范数的非局部变分复原模型能更好地重构图像的细节信息,相对于L_2范数的非局部变分模型峰值信噪比提高大于1dB,图像复原性能更优。     

基于图像局部方向特性的自适应全变分去噪模型

针对全变分模型(total variation,TV)以图像的梯度信息作为去噪的尺度参数,未考虑图像局部纹理的方向性的缺点,提出了一种基于图像局部方向特性的自适应全变分去噪模型(Adaptive directional total variation,ADTV),并推导出该模型的迭代数值求解过程。在该模型中,首先,计算出图像局部方向的角度矩阵。然后,构造与图像纹理方向一致的椭圆区域代替TV模型的圆形区域。最后,通过优化最小化算法迭代求解以获得去噪后图像。通过对比实验证明,本文提出的模型取得了更高的峰值信噪比,去噪过程中更好地增强了图像的细节信息。     

An effective dual method for multiplicative noise removal

The problem of multiplicative noise removal has been widely studied recently, but most models focus on the unconstrained problems. These models require knowing the prior level of noise beforehand, however, the information is not obtained in some case and the regularization parameters are not easy to be adjusted. Thus, in the paper, we mainly study an optimization problem with total variation constraint, and propose two new denoising algorithms which compute the projection on the set of images whose total variation is bounded by a constant. In the first algorithm, we firstly give the dual formula of our model, then compute the dual problem using alternating direction method of multipliers. Experimental results show that our method is simple and efficient to filter out the multiplicative noise when the prior of noise is unknown.