医用超声图像有效去噪方法研究与仿真

研究医用超声图像的去噪问题,保证去噪有效性.超声图像成像过程中,超声波到达器官组织散射回波信号会在超声图像中产生乘性斑点噪声,对图像质量造成破坏,传统的维纳滤波方法不能有效去除乘性斑点噪声保留图像细节信息,使超声图像去噪有效性不高.为解决上述问题,提出阿尔法超声图像去噪方法,首先将超声图像进行分析和小波分解,发现超声图像的信号具有明显的非高斯特性且二维小波系数服从阿尔法分布,然后据阿尔法分布特点采用最小平均绝对误差准则的估计器去除超声图像中的噪声.仿真结果表明,改进方法能够有效去除超声图像中的乘性斑点噪声,且最大限度保留图像细节信息,保证超声图像去噪的有效性.     

利用多小波的改进多层阈值对超声图像降噪

医学超声图像存在特有的斑点噪声,大大降低了图像质量,必须进行降噪处理。多小波具有比单小波分解更加精确、去噪效果更好的特点。对超声图像进行分形插值多小波分解,根据多小波分解后的能量分布特性,提出了改进多层阈值与模糊聚类相结合方法,将小波系数模糊聚类分成噪声和信号两类,然后在不同尺度对信号小波系数进行不同阈值萎缩处理,实现降噪目的。结果表明该方法优于硬阈值和软阈值法,可有效地降低图像斑点噪声并保留图像细节。     

基于平衡正交多小波的超声图像降噪研究

医学超声图像存在特有的斑点噪声,它大大降低了超声图像的质量,因此必须进行降噪处理。平衡正交多小波同时满足正交性和对称性,具有比单小波分解更加精确、去噪效果更好的特点。故对超声图像进行平衡正交多小波分解,然后利用模糊聚类与半软阈值相结合方法对小波系数进行萎缩处理,实现降噪目的。结果表明该方法优于硬阈值和软阈值方法,可以有效地降低原图像的斑点噪声并保留图像细节。     

基于冗余小波变换的医学超声图像去斑噪算法

医学超声图像中固有的斑点噪声严重降低了图像的可解译程度,影响了后续的图像分析和诊断。提出了一种基于冗余小波变换的超声图像去斑算法,首先对含斑图像进行对数变换,将乘性噪声变成加性噪声;再对转换后图像做冗余小波分解;在小波系数服从广义高斯分布的前提下,计算每个小波高频子带的贝叶斯萎缩阈值,利用软阈值方法修正小波系数。实验结果表明,该算法去斑性能优于传统的空间域滤波和正交小波阈值去噪方法。     

小波变换在身份证图像降噪中的应用研究

针对非理想身份证图像中存在的斑点噪声,利用小波变换降噪的特点,将身份证图像进行小波分解,然后分别采用硬阈值、软阈值和小波指数函数三种方法进行降噪处理。结果表明小波指数方法是较优降噪方法,可以有效地降低原图像的斑点噪声并保留图像细节。     

基于小波分析的超声医学图像非线性增强

首先将超声医学图像投影到小波变换域,然后利用软阈值技术方法进行降噪处理,最后使用非线性增强技术提高图像对比度。处理结果有效地去除原图像的斑点噪声,使图像中较模糊、对比度差的细节得到增强,优于传统的直方图均衡增强方法。     

图像分解模型在医学图像增强中的应用

医学图像增强是医学图像处理中的重要环节。通过分析小波去噪和ROF模型的缺陷,先利用ROF分解模型将医学图像分解成为轮廓部分和细节及噪声部分,然后对轮廓部分进行保留,接着考虑到小波系数的非高斯性,对细节和噪声进行了小波去噪,并从中提取了图像的细节部分,最后将之前的轮廓部分与之后的细节部分进行叠加。实验结果表明,本文的算法具有较高的峰值信噪比和较高的边缘保持度。     

心脏超声波图像斑点噪声去除方法仿真分析

研究心脏超声波图像质量优化问题,由于心脏的组成十分复杂,心血管各部分大小、厚度及其的不均匀,心脏瓣膜运动状况受到疾病影响也不规律,使得心脏在进行超声波图像采集过程中,往往存在大量的斑点噪声。传统的去噪算法都是将噪声转换成加信噪声,加以去除,但是转换过程会造成图像的大量有用信息损失,去噪结果缺陷明显。提出利用总体最小二乘模型的心脏超声波图像斑点噪声去除方法。根据最小二乘法相关原理,将最小二乘法相关原理应用到心脏超声波图像斑点噪声去除过程中,建立总体最小二乘模型,针对该模型的输出结果进行直交投影变换处理,实现心脏超声波图像斑点噪声的去噪处理。实验结果表明,利用改进算法进行心脏超声波斑点噪声去除处理,能够极大的提高图像的分辨率,满足心脏临床医学诊断和治疗的实际需求。     

基于小波的红外图像去噪算法研究

研究红外图像优化预处理问题,针对红外图像噪声分布的特点,提出一种新的去噪算法.对红外图像进行两次小波分解与重构,对分解后小波系数采用不同的阈值策略,分别消除加性噪声和乘性噪声.由于乘性噪声与红外图像相关,利用红外图像与乘性噪声在统计上独立的特点,在进行第二次小波分解之前先对含有乘性噪声的红外图像进行对数运算,这样才能消除乘性噪声.仿真结果表明,无论是视觉效果,还是客观评价指标,采用上述算法均优于传统去噪算法.并可广泛应用于红外成像领域.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

卡通纹理分解和全变分梯度算法实现图像恢复

为了研究图像恢复技术,提出采用卡通纹理分解和基于全变分的广义加速临近梯度算法实现图像恢复。将原始模糊图像分解成卡通部分和纹理部分,卡通部分主要是图像的低频成分受噪声干扰小,纹理部分主要是图像的高频成分受噪声干扰大,采用基于全变分的广义加速临近梯度算法进行图像去模糊和去噪,卡通部分选择较小的正则化参数,纹理部分选择较大的正则化参数,将恢复的卡通部分和纹理部分进行合成得到恢复图像。通过对两张标准测试图像的MATLAB实验仿真,证明了该方法不仅收敛速度快而且效果比一般的临近梯度算法要好,尤其适合于恢复模糊度不是很高的图像。     

脊波域的图像数字水印算法研究

提出一种将混沌置乱加密后的水印信息嵌入到图像脊波变换域的算法。将图像分块后进行有限脊波变换,选择出图像纹理块的脊波域最大能量方向上的系数嵌入水印,水印提取只需要记录嵌入位置序列即可。实验结果表明,算法对噪声、压缩、不规则剪切、对比度等攻击有较强的鲁棒性。     

一种新的基于稀疏分解的图像放大方法

提出了一对新的冗余离散小波变换(RDWT)和波原子变换(WAT)字典,并将其应用于图像稀疏形态成分分解以获得图像的卡通与纹理成分.并针对卡通和纹理所具有的不同形态学特征,对卡通成分采用具有曲率运动、边缘冲击特性和平滑去噪性能的非线性self-snake模型来放大;对纹理成分采用双三次插值方法来放大,最后通过叠加就可获得放大图像.实验结果表明,这种基于新字典对的稀疏形态成分分解的图像放大方法相比于传统的基于整幅图像的放大方法能够有效地保护小曲率和大梯度,强化图像边缘,保证纹理细节清晰完整.     

Adaptive variational models for image decomposition

Image decomposition refers to the splitting of an image into two or more components.In this paper,a clean image is separated into two parts:one is the cartoon component,consisting only of geometric structure,and the other is the oscillatory component,consisting of texture.Three parts for noisy image are considered:cartoon,texture,and noise.To better decompose an image,we propose two new variational models.In our models,two adaptive regularization terms are introduced.The two regularization terms are determined by an adaptive function which can discriminate the cartoon and texture of an image automatically.Experimental results illustrate the efectiveness of the proposed models for image decomposition.     

Image denoising with complex ridgelets

In this paper, we propose a novel image denoising method by incorporating the dual-tree complex wavelets into the ordinary ridgelet transform. The approximate shift invariant property of the dual-tree complex wavelet and the high directional sensitivity of the ridgelet transform make the new method a very good choice for image denoising. We apply the digital complex ridgelet transform to denoise some standard images corrupted with additive white noise. Experimental results show that the new method outperforms VisuShrink, the ordinary ridgelet image denoising, and wiener2 filter both in terms of peak signal-to-noise ratio and in visual quality. In particular, our method preserves sharp edges better while removing white noise. Complex ridgelets could be applied to curvelet image denoising as well.     

基于形态字典学习的复杂背景SAR图像舰船尾迹检测

SAR图像舰船尾迹检测不仅可用于反演运动舰船的航速航向信息,也有助于发现弱小舰船目标.然而现有舰船尾迹检测方法一般仅适用于简单海况背景下的SAR图像,复杂海况背景下的检测效果难以满足应用需求.本文提出一种基于形态成分分析与多字典学习的复杂背景舰船尾迹检测方法.该方法针对海况背景的复杂多变性以及舰船尾迹类型的有限性,通过离线学习方式构建海面纹理字典,通过解析方式构建尾迹结构字典并迭代更新,将图像分解为包含舰船尾迹的结构成分与包含海面背景的纹理成分,利用剪切波变换对结构成分高频系数重构以增强结构成分,并通过Radon变换对增强后的结构成分进行尾迹线检测.实验结果表明,本文所提方法对于复杂背景SAR图像舰船尾迹检测的效果明显优于现有方法.     

测度熵在图像纹理分析中的应用

本文的主要目的是把测度熵应用在图像的纹理分析中。测度熵比基于统计的方法要可靠、方便。把它应用于对纯卡通图、扫描卡通图、混合图以及照片的识别,大大提高了图像自动生成乡花针脚软件的价值。另外,测度熵还能有效地检测出图像纹理的光滑度,对绣花软件来说,我们就可以根据光滑度从针脚库中自动选出合适的针脚,使得绣图更加逼真、自然。     

Image fusion with morphological component analysis

Image fusion can produce a single image that describes the scene better than the individual source image. One of the keys to image fusion algorithm is how to effectively and completely represent the source images. Morphological component analysis (MCA) believes that an image contains structures with different spatial morphologies and can be accordingly modeled as a superposition of cartoon and texture components, and that the sparse representations of these components can be obtained by some specific decomposition algorithms which exploit the structured dictionary. Compared with the traditional multiscale decomposition, which has been successfully applied to pixel-level image fusion, MCA employs the morphological diversity of an image and provides more complete representation for an image. Taking advantage of this property, we propose a multi-component fusion method for multi-source images in this paper. In our method, source images are separated into cartoon and texture components, and essential fusion takes place on the representation coefficients of these two components. Our fusion scheme is verified on three kinds of images and compared with six single-component fusion methods. According to the visual perceptions and objective evaluations on the fused results, our method can produce better fused images in our experiments, compared with other single-component fusion methods.     

A novel multi-focus image fusion approach based on image decomposition

Multi-focus image fusion is an effective technique to integrate the relevant information from a set of images with the same scene, into a comprehensive image. The fused image would be more informative than any of the source images. In this paper, a novel fusion scheme based on image cartoon-texture decomposition is proposed. Multi-focus source images are decomposed into cartoon content and texture content by an improved iterative re-weighted decomposition algorithm. It can achieve rapid convergence and naturally approximates the morphological structure components. The proper fusion rules are constructed to fuse the cartoon content and the texture content, respectively. Finally, the fused cartoon and texture components are combined to obtain the all-in-focus image. This fusion processing can preserve morphological structure information from source images and performs few artifacts or additional noise. Our experimental results have clearly shown that the proposed algorithm outperforms many state-of-the-art methods, in terms of visual and quantitative evaluations.     

A new approach for rotation-invariant and noise-resistant texture analysis and classification

The analysis and classification of images, such as texture images, is one of the substantial and important fields in image processing. Due to destructive effects of image rotation and noise, the stability and efficiency of texture analysis and classification methods are an important research area. In this paper, a new method for texture analysis and classification has been proposed which is based on a particular combination of wavelet, ridgelet and Fourier transforms as well as support vector machine. The proposed method has been evaluated for 13 texture datasets produced by three original datasets containing 25 and 111 original textures from Brodatz database and 24 original textures from OUTEX database. These datasets comprise 415584 and 93600 rotated noise-free and noisy texture images for Brodatz database and also 49920 noisy and 4320 noise-free texture images for OUTEX database, respectively. Simulation results demonstrate the capability, efficiency and also stability of the proposed method especially for real-time rotation-invariant and noise-resistant texture analysis and classification.