一种新的基于图像边缘的插值算法

灰度图像放大时,插值所具有的平滑作用会退化图像的高频部分,使放大图像轮廓变得模糊,该文提出了一种新的图像插值算法．先用一阶微分运算分离出图像的平坦区域和边缘区域,对图像的平坦区域采用双线性插值法,对图像的边缘部分采用本文的插值算法,实验证明该算法有效地保持了边缘信息,得到了视觉信息较好的插值图像。     

一种新的基于图像边缘的插值算法

灰度图像放大时,插值所具有的平滑作用会退化图像的高频部分,使放大图像轮廓变得模糊,该文提出了一种新的图像插值算法,先用一阶微分运算分离出图像的平坦区域和边缘区域,对图像的平坦区域采用双线性插值法,对图像的边缘部分采用本文的插值算法,实验证明该算法有效地保持了边缘信息,得到了视觉信息较好的插值图像。     

图像平滑算法的研究与改进

图像平滑作为去除图像中含有噪声的图像增强处理技术,是各种与图像有关软件中必不可少的功能模块。文中在分析噪声模型的基础上,对均值平滑、中值平滑、边缘保持平滑等技术从图像处理空间域中的算法原理、实现方法及效率等方面进行了详细比较。针对图像中含有的不同噪声模型,通过多次试验,得出了值得从事图像处理研究者参考的结论。这将有助于提高图像处理软件开发的效率。     

基于MAP框架的时空联合自适应视频序列超分辨率重建

超分辨率重建在视频监控、高清晰度电视、遥感图像、医学图像处理等领域具有广阔的应用前景. 最大后验估计(maximum a posteriori, MAP)法是普遍采用的一种超分辨率重建方法. 针对传统MAP法存在的局限性, 本文提出了一种基于MAP框架的时空联合自适应视频序列超分辨率重建算法. 时空联合自适应机制的引入使得算法在保持边缘的同时可减小错误运动估计矢量对重建图像质量的影响. 实验结果表明, 算法具有重建质量好、边缘保持能力强、收敛速度快等特点.     

An Edge-Preserving Image Reconstruction Using Neural Network

This paper presents an image restoration model based on the implicit function theorem and edge-preserving regularization. We then apply the model on the subband-coded images using the artificial neural network. The edge information is extracted from the source image as a priori nowledge to recover the details and reduce the ringing artifact of the subband-coded image. The multilayer perceptron model is employed to implement the restoration process. The main merit of the presented approach is that the neural network model is massively parallel with strong robustness for the transmission noise and parameter or structure perturbation, and it can be realized by VLSI technologies for real-time applications. To evaluate the performance of the proposed approach, a comparative study with the set partitioning in hierarchical tree (SPIHT) has been made by using a set of gray-scale digital images. The experimental results showed that the proposed approach could result in compatible performances compared with SPIHT on both objective and subjective quality for lower compression ratio subband coded image.     

十字基自适应指导滤波在图像降噪中的应用

针对于传统线性滤波器在非边界区域与边界区域有着滤波平滑和边缘保护的矛盾,提出了一种基于指导滤波和十字基相结合的算法。其主要思想是对指导滤波加入自适应偏移量进行调整,保证在边缘结构处细节的保留;普通指导滤波的计算邻域为固定大小的方形窗,在自适应指导滤波算法邻域窗的选取中引入十字基滤波窗,以灰度相似性作为阈值,利用立体匹配的优势,生成大小可自动调节、形状可自适应调整的滤波块,从而实现了滤波性能的提高;偏移量的选取值随滤波窗灰度阈值正比变化,比硬阈值有更好的效果。通过对国际标准图像序列的实验仿真结果比较,表明十字基滤波窗在分块方面有着较高的效率,平滑区域的滤波效果更佳,偏移量的加入使算法在边界区的纹理和细节特征得以保留;较之普通指导滤波算法,提出的十字基自适应指导滤波在峰值信噪比(PSNR)值上提高了约2 dB;通过对实拍图像的处理结果对比表明该算法在实际运用中有着更好的适用性。     

基于Snake模型的图像分割新算法

针对目前基于Snake模型的图像分割算法普遍存在噪声鲁棒性差、适用范围受限、易发生弱边缘泄露以及轮廓曲线难以收敛到细小深凹边界的缺陷,提出了一种基于Snake模型的图像分割新算法。首先,选取新的扩散项代替具有各向同性光滑作用的拉普拉斯算子;其次,引入p-拉普拉斯泛函到平滑能量项中强化法线方向外力;最后,利用边缘保护项使外力场方向与边缘方向一致,以防止弱边缘泄漏并促使轮廓线收敛到细小深凹边界。实验结果表明,所提模型不仅克服了现有基于Snake模型的图像分割算法的缺陷,具有更好的分割效果,明显提高了抗噪性能和角点定位精度,而且耗时更少,适用于噪声图像、医学图像以及含有很多弱边缘的自然图像分割。     

基于自适应边缘保持算法的图像插值

为了较好地保持图像的边缘信息,提出一种新的基于线性预测模型的边缘保持算法．该方法根据低分辨图像和高分辨图像之间的几何对偶特性(即图像的边缘在不同分辨率下具有不变性),用低分辨线性预测模型取代高分辨线性预测模型,以此来计算最优插值系数向量．通过具体的例子验证了该模型边缘保持的有效性．实验结果表明,该方法具有较好的自适应性、边缘保持特性和较低的计算复杂度．     

Application of the split-gradient method to 3D image deconvolution in fluorescence microscopy

The methods of image deconvolution are important for improving the quality of the detected images in the different modalities of fluorescence microscopy such as wide‐field, confocal, two‐photon excitation and 4Pi. Because deconvolution is an ill‐posed problem, it is, in general, reformulated in a statistical framework such as maximum likelihood or Bayes and reduced to the minimization of a suitable functional, more precisely, to a constrained minimization, because non‐negativity of the solution is an important requirement. Next, iterative methods are designed for approximating such a solution. In this paper, we consider the Bayesian approach based on the assumption that the noise is dominated by photon counting, so the likelihood is of the Poisson‐type, and that the prior is edge‐preserving, as derived from a simple Markov random field model. By considering the negative logarithm of the a posteriori probability distribution, the computation of the maximum a posteriori (MAP) estimate is reduced to the constrained minimization of a functional that is the sum of the Csiszár I‐divergence and a regularization term. For the solution of this problem, we propose an iterative algorithm derived from a general approach known as split‐gradient method (SGM) and based on a suitable decomposition of the gradient of the functional into a negative and positive part. The result is a simple modification of the standard Richardson–Lucy algorithm, very easily implementable and assuring automatically the non‐negativity of the iterates. Next, we apply this method to the particular case of confocal microscopy for investigating the effect of several edge‐preserving priors proposed in the literature using both synthetic and real confocal images. The quality of the restoration is estimated both by computation of the Kullback–Leibler divergence of the restored image from the detected one and by visual inspection. It is observed that the noise artefacts are considerably reduced and desired characteristics (edges and minute features as islets) are retained in the restored images. The algorithm is stable, robust and tolerant at various noise (Poisson) levels. Finally, by remarking that the proposed method is essentially a scaled gradient method, a possible modification of the algorithm is briefly discussed in view of obtaining fast convergence and reduction in computational time.     

一种改进的双边滤波算法

双边滤波是一种既可有效降低图像加性噪声又可保持图像边缘细节的滤波技术,它能同时利用邻域内像素点的空间邻近度信息和亮度像似度信息。然而,双边滤波器的2维实现方式和浮点型空间邻近度因子不利于硬件实现,且计算量大。提出了一种改进的双边滤波算法,该算法设计了整数型空间邻近度因子,使用水平和垂直方向上的1维滤波实现方式,并能自适应地设置空间邻近度控制参数的值。实验结果表明,改进的双边滤波算法可获得较好的滤波效果。