改进混合策略的多分辨率3D医学图像配准

基于混合策略的多分辨率算法是当前3D医学图像刚体配准中普遍采用的方法,不过其仅仅是优化算法的混合。通过研究不同分辨率对一阶互信息（常称为互信息）和二阶互信息配准的影响,在二级多分辨率策略的配准中,各级采用相对更适合的相似性测度,提出了混合优化算法和混合测度的改进算法。实验表明,改进算法在配准精度上达到了亚体素级,且明显优于基于单一测度的算法,在配准速度上远远快于基于二阶互信息单一测度的算法,略慢于基于一阶互信息单一测度的算法。     

3D surface filtering using spherical harmonics

This paper presents a novel approach for 3D surface filtering over two-manifold meshes. A robust spherical parameterization algorithm is proposed to transform the input surface into a spherical vector function/signal. This signal is then decomposed into frequency domain using spherical harmonic transforms. Finally, traditional filtering techniques are generalized to process such spherical signals in either the frequency or spatial domain. Our major contribution is the two-phase spherical parameterization algorithm, which can handle meshes with complex shapes by incorporating local parameterization into the progressive mesh. A number of experimental examples demonstrate the potential of our algorithm.     

小波域三维块匹配图像去噪

提出了一种关于图像去噪的三维块匹配算法（BM3D算法）的改进算法。它不仅保留了三维块匹配算法好的性质,而且最大的优点是能大大减少计算量,缩短运算时间。算法包括三个步骤：首先,对含噪图像进行小波分解;其次,对小波分解后的高频分量用三维块匹配（BM3D）算法进行去噪处理;最后,用处理后的结果进行小波重构得到去噪图像。给出了该算法的详细实现过程,并把它与以前的三维块匹配算法进行了比较。结果表明,改进后的算法,不但保留了三维块匹配算法在去噪方面好的性质,而且大大减少了运算量。     

侧扫声呐图像的3维块匹配降斑方法

斑点噪声是影响侧扫声呐图像质量的主要因素,降斑处理对侧扫声呐图像的判别与分析非常重要。针对侧扫声呐图像自身特性和斑点噪声分布特点,提出一种基于3维块匹配(BM3D)的降斑方法。根据海底散射模型,得到侧扫声呐图像斑点噪声的瑞利分布模型,然后通过高斯光滑函数幂变换将瑞利分布的噪声转化为高斯分布,通过对数变换将乘性噪声转变为加性噪声,再进行自适应的BM3D滤波,最后采用逆变换得到降斑图像。实验结果表明,该方法在降噪、边缘和纹理保持等方面均优于空间域、小波域、Curvelet域的一些降斑方法。     

一种改进的医学图像各向异性扩散滤波算法

医学图像（CT、MRI）的滤波处理,须保留具有重要诊断意义的边缘细节信息。针对Perona-Malik各向异性扩散模型病态且不稳定的不足,提出了一种改进的各向异性扩散滤波算法。通过采用自适应加权的多尺度形态滤波来改进扩散系数,建立了一个对噪声图像更有效和更具适应性的去噪扩散模型。同时引入迭代终止准则,避免了迭代次数的设定。实验结果表明,算法优于PM方法和Catte方法,在提高信噪比的同时又可保留重要的微细结构,可以较好地满足医学图像的使用要求。     

结合频谱扩散的指纹图像滤波增强

目的 指纹图像增强是自动指纹识别系统中的重要环节,是获取可靠的指纹细节特征的基础。为了弥补指纹图像的质量缺陷,实现对低质量指纹图像的有效增强,提出一种基于块质量评价和块频谱匹配滤波器的块频谱扩散指纹增强方法。方法 首先,基于块内点方向一致性参数,给出了块质量评价结果;然后,研究了复合窗口频谱扩散特性,完成了基于一次模糊K均值聚类、一阶自适应带宽的2维巴特沃斯带通滤波器和2维高斯窗的块频谱匹配滤波器设计;最后,依据块质量等级的高低,应用块频谱匹配滤波器对复合窗口的块频谱进行滤波增强。结果 选取FVC 2004指纹库中的大量指纹图像进行了实验,实验结果表明,该方法对高质量和低质量指纹图像均有良好的增强效果。结论 本文方法对指纹的脊线方向和频率等参数依赖较弱,拥有很强的容错能力。实验结果验证了该方法在指纹图像增强中的有效性,可适用于高质量和低质量的指纹图像增强。     

Image filtering using multiresolution representations

It is shown how multiresolution representations can be used for filter design and implementation. These representations provide a coarse frequency decomposition of the image, which forms the basis for two filtering techniques. The first method, based on image pyramids, is used for approximating the convolution of an image with a given mask. In this technique, a filter is designed using a least-squares procedure based on filters synthesized from the basic pyramid equivalent filters. The second method is an adaptive noise reduction algorithm. An optimally filtered image is synthesized from the multiresolution levels, which in this case are maintained at the original sampling density. Individual pixels of the image representation are linearly combined under a minimum mean square error criterion. This uses a local signal-to-noise ratio estimate to provide the best compromise between noise removal and resolution loss     

运动补偿时域滤波中基于残差图像滤波的更新策略

现有的比较成熟的可伸缩编码框架是基于运动补偿时域滤波技术的。改进了运动补偿时域滤波中的更新操作，并引入去块滤波。更新操作过程中，要求更新块中的像素所对应的预测运动向量完全一致，否则不参与更新操作。更新操作前，对残差图像在边缘级和采样点级进行滤波控制。通过对高通信号的去块滤波，减少更新块中的伪边界成分，同时减轻块效应对后继预测操作的影响，改善编码效率。实验结果表明该方法能够改进图像的主观质量，提高低运动量的图像序列的编码性能。     

利用偏振滤波的自动图像去雾

针对雾天退化图像提出一种自适应图像复原方法。 该方法基于定义的偏振图像暗通道, 自动提取图像中的天空区域, 由此获得大气光的强度和偏振度; 采用偏振滤波提取大气光强信息, 并基于最小归一化互信息原则对估计的大气光偏振度进行优化; 根据大气光强的变化规律, 对大气光强的分布进行修复; 将大气光强作为加性噪声予以扣除, 并补偿因大气衰减带来的影响, 最终复原得到场景的辐射强度信息。 实验结果表明, 该方法能够有效地改善雾天下图像的退化现象, 提高了图像的清晰度。     

改进各向异性扩散模型在图像滤波去噪中的应用

为了增强锅炉水位计图像滤波去噪效果,提高图像清晰度,便于后期液位计图像识别研究,通过分析P-M各向异性扩散模型、选择扩散模型及You Yu-Li和Kaveh M四阶偏微分方程的滤波去噪算法,提出了改进各向异性扩散模型滤波算法.所提算法对Perona和Malik两个扩散函数均值化,并引入标准差作为梯度期望值的偏差裕度,结合了P-M各向异性扩散模型保边缘特性的优点,并消除了由于传统各向异性滤波算法迭代过度所造成的阶梯缺陷问题,确保图像有用信息不缺失和像素点平滑度.实验结果表明:所提算法能够更好地降低噪声对目标信号提取产生的影响,提高了图像识别鲁棒性,增强了图像平滑滤波效果,保证了锅炉水位计图像边缘清晰度和完整性.     

2D/3D image registration using regression learning

In computer vision and image analysis, image registration between 2D projections and a 3D image that achieves high accuracy and near real-time computation is challenging. In this paper, we propose a novel method that can rapidly detect an object’s 3D rigid motion or deformation from a 2D projection image or a small set thereof. The method is called CLARET (Correction via Limited-Angle Residues in External Beam Therapy) and consists of two stages: registration preceded by shape space and regression learning. In the registration stage, linear operators are used to iteratively estimate the motion/deformation parameters based on the current intensity residue between the target projection(s) and the digitally reconstructed radiograph(s) (DRRs) of the estimated 3D image. The method determines the linear operators via a two-step learning process. First, it builds a low-order parametric model of the image region’s motion/deformation shape space from its prior 3D images. Second, using learning-time samples produced from the 3D images, it formulates the relationships between the model parameters and the co-varying 2D projection intensity residues by multi-scale linear regressions. The calculated multi-scale regression matrices yield the coarse-to-fine linear operators used in estimating the model parameters from the 2D projection intensity residues in the registration. The method’s application to Image-guided Radiation Therapy (IGRT) requires only a few seconds and yields good results in localizing a tumor under rigid motion in the head and neck and under respiratory deformation in the lung, using one treatment-time imaging 2D projection or a small set thereof.     

基于3D-LSCM的图像混沌加密算法

提出了一种基于三维Logistic-Sine级联映射(3D-LSCM)的图像混沌加密算法。首先,将Logistic映射的输出作为Sine映射的输入,从而构造出一种三维Logistic-Sine级联映射。其次,利用3D-LSCM设计一种图像混沌加密算法,该算法采用两轮置乱、双向扩散机制,并且将明文信息作为初始条件的一部分,可增强等价密钥破译的难度。安全性能分析和数值仿真结果证实了该算法的可行性。     

基于Canny算子的边缘切线扩散医学超声图像去噪

医学超声图像的细节特征在临床诊断中具有重要的意义.针对于传统的PM算法以及各种改进型各向异性去噪方法(Catte_PM、SRAD、CENCD等)存在边缘中的噪声点未作处理,多次迭代产生虚假边缘等缺点,通过分析具有代表性的Catte_PM各向异性模型,提出了一种结合自适应Canny算子,沿图像边缘切线方向扩散的去噪方法.该算法首先通过改进的Canny算子将图像范围分为边缘区和非边缘区;其次改进现有的扩散方法,使扩散方向只沿图像边缘切线方向进行;最后对非边缘区域采用有限次(三次)的各向同性滤波.实验结果表明,该方法能够有效地解决滤波和图像细节保护这一矛盾问题,使得图像质量有较明显的改善.     

基于改进各向异性扩散的超声医学图像滤波方法

为了能有效地去除超声医学图像中的噪声,又能较好地保持图像的边缘和重要细节信息,在η-ξ正交坐标系下研究并分析了各向异性扩散模型(P-M模型)的扩散滤波机制,并在此坐标系下建立了一种新的各向异性扩散滤波方案。实验结果表明,改进的扩散模型不仅能够有效地保持图像边缘,而且还能够克服P-M模型对小尺寸噪声的敏感问题。     

基于各向异性扩散的医学图像分水岭分割算法

针对医学图像形态建模过程易产生过分割的问题,提出了一种基于各向异性扩散的分水岭分割算法。该算法首先对原始图像进行自适应各向异性扩散滤波,然后引入多尺度的形态梯度图像作为分水岭变换的参考图像,以突出图像中物体的边界轮廓,平滑具有均匀亮度的区域。最后,定义基于边界平均灰度和面积的区域合并准则,对分割后的区域进一步合并。实验结果表明,该算法能有效抑制过分割,具有较强的抗噪声性能,得到的分割结果可以满足医学图像建模的需要。     

Low and non-uniform illumination color image enhancement using weighted guided image filtering

In the state of the art,grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumi...     

Image spam filtering using convolutional neural networks

Spammers often embed text into images in order to avoid filtering by text-based spam filters, which result in a large number of advertisement spam images. Garbage image recognition has become one of the hotspots in the field of Internet spam filtering research. Its goal is to solve the problem that traditional spam information filtering methods encounter a sharp performance decline or even failure when filtering spam image information. Based on the clustering algorithm, this paper proposes a method to expand the data samples, which greatly improves the number of high-quality training samples and meets the needs of model training. Then, we train a convolutional neural networks using the enlarged data samples to recognize the SPAM in real time. The experimental results show that the accuracy of the model is increased by more than 14% after using the method of data augmentation. The accuracy of the model can be improved by 6% compared with other methods of data augmentation. Combined with convolutional neural networks and the proposed method of data augmentation, the accuracy of our SPAM filtering model is 7–11% higher than that of the traditional method.     

Fast evolution of image manifolds and application to filtering and segmentation in 3D medical images

In many instances, numerical integration of space-scale PDEs is the most time consuming operation of image processing. This is because the scale step is limited by conditional stability of explicit schemes. We introduce the unconditionally stable semiimplicit linearized difference scheme that is fashioned after additive operator split (AOS) [Weickert, J. et al. (1998)], [Goldenberg, R et al., (2001)] for Beltrami and the subjective surface computation. The Beltrami flow [Kimmel, R. (1997) (1999)], [Sochen, N. et al. (1998)], is one of the most effective denoising algorithms in image processing. For gray-level images, we show that the flow equation can be arranged in an advection-diffusion form, revealing the edge-enhancing properties of this flow. This also suggests the application of AOS method for faster convergence. The subjective surface [Sarti, A. et al. (2002)] deals with constructing a perceptually meaningful interpretation from partial image data by mimicking the human visual system. However, initialization of the surface is critical for the final result and its main drawbacks are very slow convergence and the huge number of iterations required. We first show that the governing equation for the subjective surface flow can be rearranged in an AOS implementation, providing a near real-time solution to the shape completion problem in 2D and 3D. Then, we devise a new initialization paradigm where we first "condition" the viewpoint surface using the fast-marching algorithm. We compare the original method with our new algorithm on several examples of real 3D medical images, thus revealing the improvement achieved.     

3D symmetry detection using the extended Gaussian image

Symmetry detection is important in the area of computer vision. A 3D symmetry detection algorithm is presented in this paper. The symmetry detection problem is converted to the correlation of the Gaussian image. Once the Gaussian image of the object has been obtained, the algorithm is independent of the input format. The algorithm can handle different kinds of images or objects. Simulated and real images have been tested in a variety of formats, and the results show that the symmetry can be determined using the Gaussian image