基于局部最大和最小强度先验的遥感图像盲去模糊

针对遥感图像的运动模糊问题,提出一种基于局部最大和最小强度先验的遥感图像盲去模糊方法。该方法利用遥感图像局部像素强度的稀疏性作为先验条件,使用简单的迭代阈值收缩方法求解潜像和模糊核,再由非盲反卷积算法得到去模糊图像。实验结果表明,所提方法能提高计算效率,对于可见、红外遥感图像,均能有效恢复图像的纹理细节,抑制伪影,提升了复原图像的主观效果与客观评价指标。     

基于暗通道与低秩先验的运动模糊图像盲复原

摘`要:为了将运动模糊图像恢复成清晰图像,提出了一种基于暗通道与低秩先验的图像盲去模糊方法。首先,利用图像暗通道的稀疏性进行中间复原图像估计,同时引入带权值的低秩先验约束抑制中间复原图像中的噪声,提高模糊核估计的准确性;然后,通过交替迭代的优化策略得到准确的模糊核;最后,用Hyper-Laplacian先验方法得到清晰图像。实验结果表明,提出的方法能有效抑制噪声和振铃效应,保留图像细节,对运动模糊图像有较好的复原效果。     

基于边缘信息图像条件的盲去模糊算法

本文主要研究了基于边缘信息图像条件的盲去模糊算法,基于梯度稀疏性与局部自回归模型研究图像非盲去模糊算法的具体计算应用。     

基于边缘信息的运动模糊图像的鲁棒盲复原

为获得适用于不同模糊图像且简捷的图像盲复原方法,提出了一种稳健的从单幅模糊图像中求取模糊核并对图像去模糊的图像盲复原方法。根据模糊图像与非模糊图之间的边缘关系求模糊核,并在多尺度框架下针对各个子算法设定自适应参数,从而构建一个稳健的图像盲复原方法。对复原结果用4种无参考的图像质量评价方法的评价结果显示,本文方法在噪声和...     

基于变分贝叶斯估计的相机抖动模糊图像的盲复原算法

在曝光过程中由于相机抖动而导致的运动模糊,是一种常见的图像降质现象。该文提出了一种基于变分贝叶斯估计和自然图像梯度统计特性的盲复原算法,用于恢复相机抖动模糊图像,同时针对图像复原过程中出现的振铃效应,设计了一种基于分区域检测和Fuzzy滤波器的去振铃效应方法。实验结果表明,该文提出的盲复原算法能够有效地去除图像中因相机抖动而产生的模糊,而且在保持图像边缘和细节的同时,可以较好地降低振铃效应对图像复原质量的影响。     

联合深度先验图像去模糊方法

针对现有深度学习图像去模糊方法依赖大量训练数据的问题,从图像先验信息出发,提出一种联合深度先验图像去模糊方法。用自编码-自解码网络和全连接网络,分别对潜像和模糊核的深度先验进行建模,在去模糊模型中加入TV(Total Variation)正则化和冲击滤波进行改进,从而提高方法的抗噪性能,增强复原图像细节,最后通过联合优化得到清晰图像。实验结果表明,该方法可以在不依赖大量训练数据的基础上进行图像去模糊,在公开数据集上获得的峰值信噪比值为29.24dB,结构相似度值为0.862,与其他方法相比在真实模糊图像上去模糊效果更好。     

基于清晰图像先验知识的盲复原算法

图像采集系统在获取图像时,受到各种干扰,使图像变模糊并含有噪声。为此,提出了一种基于清晰图像先验知识的图像盲复原算法。首先通过统计分析大量的清晰图像梯度域的概率分布特性,将图像划分为具有较多复杂纹理的区域和平坦区域,拟合概率分布函数,得到清晰图像的先验知识,并加入图像局部约束条件,即复原图像平坦区域与退化图像的平坦区域的概率分布变化较小,避免产生振铃现象。然后,建立图像噪声模型,将图像噪声划分为高斯噪声和均匀分布噪声,防止退化图像中的灰度过饱和像素点在复原过程中产生异常值。最后,利用最大后验概率模型,构造代价函数,将图像盲复原问题转变为求代价函数最小化问题,并用最大期望算法和快速迭代收缩算法求解代价函数,恢复出清晰图像。实验结果表明,该算法能有效恢复图像细节,锐化图像边缘,抑制噪声,避免产生振铃效应。     

凹凸范数比值正则化的快速图像盲去模糊

模糊图像可表示为清晰图像和模糊核函数的卷积,由模糊图像恢复出清晰图像,需要同时估计模糊核和清晰图像,因此是一个病态问题.优化含有先验项的代价函数是求解病态问题最有效方法之一.针对图像盲去模糊问题,本研究提出具有更强稀疏表达能力的凹凸范数比值正则化先验项,在用变量分裂法求解模型时,提出用L1范数保真项更新估计图像,在更新模糊核时,提出使用线性递增权重参数对模糊核按多尺度方法由粗到细逐步估计,当获得模糊核后,利用封闭阈值公式估计清晰图像.该方法能快速得到高质量的清晰图像,实验结果验证了模型的有效性和算法的快速性.     

基于多尺度编解码器网络的运动图像盲去模糊

为避免因模糊核估计不准确而使得复原后的图像易造成欠去模糊或过度去模糊等问题,提出了一种基于多尺度编解码器网络去模糊模型.首先,在传统的编解码器网络中加入跳跃连接和多尺度循环连接,结合各层图像特征信息的同时使网络训练得更加稳定.其次,将提出的编解码器网络与改进的嵌套残差网络结合,采用由"粗"到"精"的方法进一步提取不同尺...     

Improved image deblurring based on salient-region segmentation

Image deblurring techniques play important roles in many image processing applications. As the blur varies spatially across the image plane, it calls for robust and effective methods to deal with the spatially-variant blur problem. In this paper, a Saliency-based Deblurring (SD) approach is proposed based on the saliency detection for salient-region segmentation and a corresponding compensate method for image deblurring. We also propose a PDE-based deblurring method which introduces an anisotropic Partial Differential Equation (PDE) model for latent image prediction and employs an adaptive optimization model in the kernel estimation and deconvolution steps. Experimental results demonstrate the effectiveness of the proposed algorithm.     

Image deblurring via enhanced local maximum intensity prior

The enhanced local maximum intensity prior is proposed in this paper. We find that the intermediate latent image restoration can be enhanced by joining the local maximum intensity with the local maximum gradient, and the quality of the recovered kernel is hereby improved. Our method is based on the fact that the maximum value of local patch pixels and gradients will decrease with the blurring process, which has been proved mathematically and experimentally. This joint prior has been named enhanced local maximum intensity prior. Based on this prior, a new energy function is established and the image deblurring is successfully carried out by alternating optimization strategy. Extensive experiments have been conducted, which shows that our method is more effective compared with the state-of-the-art methods.     

基于多尺度密集连接和U-Net改进的动态场景去模糊算法

针对现有去运动模糊网络在图像恢复过程中出现的纹理细节丢失、无法抑制噪声、产生振铃伪影等问题,提出一种基于多尺度密集连接和U-Net改进的动态场景去模糊算法。首先,借助U-Net网络中空洞卷积下采样有效扩大感受野,在不增加参数量的情况下避免图片产生不可逆损伤,并利用亚像素卷积在上采样过程中以小的卷积核获得清晰的图像细节,降低运算复杂度;其次,设计多尺度密集特征提取模块(multi-scale dense feature extraction, MDFE),通过密集连接的卷积层加强深层次特征提取和复用,运用空间金字塔池化(spatial pyramid pooling, SPP)分支引导多尺度特征的传递和融合,促进图像细节纹理的有效保留;最后,采用ConvLSTM双向连通结构(bidirectional convolution LSTM unit, BCLU)以非线性方式从编码路径补偿简单级联流失的上下文特征,推动深度特征跨阶段相互作用,弱化边缘伪影和噪声干扰。与现有先进方法对比,验证了本文所提算法在性能上的优势。     

逆滤波法在图像复原中的应用

针对逆滤波图像复原算法进行研究,在已知系统退化模型的情况下,对退化图像使用逆滤波算法进行复原,再对复原后的图像进行平滑处理,使其更接近于原始图像。文中使用Vis-ual C++6.0和Matlab作为开发工具,实验表明该上述算法具有简单可靠等优点,能使图像质量得到改善。     

Blind image deconvolution for single motion-blurred image

Motion blur due to camera shake during exposure is one of the most common reasons of image degradation,which usually reduces the quality of photographs seriously.Based on the statistical properties of the natural image's gradient and the blur kernel,a blind deconvolution algorithm is proposed to restore the motion-blurred image caused by camera shake,adopting the variational Bayesian estimation theory.In addition,the ring effect is one problem that is not avoided in the process of image deconvolution,and usually makes the visual effect of the restored image badly.So a dering method is put forward based on the sub-region detection and fuzzy filter.Tested on the real blurred photographs,the experimental results show that the proposed algorithm of blind image deconvolution can remove the camera-shake motion blur from the degraded image effectively,and can eliminate the ring effect better,while preserve the edges and details of the image well.     

基于Bernstein多项式的数字图像分存

数字图像分存技术将一幅秘密图像分解成为几幅无意义或者杂乱无章的图像进行存储或传输,以便增加图像信息的安全性.本文讨论了数字图像分存技术及其在信息安全中的应用,首先利用置乱变换对传输图像进行加密,然后利用Bernstein多项式对加密图像进行分存,并可以无误差的恢复出原图像.     

Split Bregmanized anisotropic total variation model for image deblurring

In this paper, an effective image deblurring model is proposed to preserve sharp image edges by suppressing the stair-casing arising in the total variation (TV) based method by using the anisotropic total variation. To solve the difficult L1 norm problems, the split Bregman iteration is employed. Several synthetic degraded images are used for experiments. Comparison results are also made with total variation and nonlocal total variation based method. Experimental results show that the proposed method not only is robust to noise and different blur kernels, but also performs well on blurring images with more detailed textures, and the stair-casing effect is well suppressed.     

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.