复脊波变换SAR图像去噪算法

在分析传统脊波变换去噪方法优缺点的基础上, 针对其不足, 提出一种基于复脊波变换的去噪方法。算法将传统脊波变换中的标量一维小波变换替换为二元树复小波变换, 使得脊波变换具有平移不变性; 然后, 对图像采用冗余分块处理, 使得处理结果更平滑, 有效地提高了图像的峰值信噪比（PSNR）。仿真实验表明, 在SAR图像去噪应用中, 本方法能够更好地保留图像中的纹理信息, 处理结果优于传统脊波变换以及小波变换去噪方法。     

基于二进脊波变换的图像去噪

二进小波变换在每次分解时不进行下采样,与小波级数相比,它是冗余的,且二进小波变换的部分系数扰动不会带来重构信号的严重失真。在相同的误判概率下,基于二进小波变换的信号去噪效果会好于基于小波级数变换的信号去噪效果。基于这个思想,该文提出了一种基于二进脊波变换的图像去噪算法。实验结果证明,与小波级数相比,该算法具有更好的去噪效果。     

改进的基于脊波变换的图像去噪新算法

脊波(Ridgelet)变换能够克服小波变换在处理高维信号时的不足,是一种有效处理二维奇异性信号的新方法;在分析传统脊波阈值去噪法的原理和存在的优缺点的基础上,构造了一种新的阈值函数,并结合SURE阈值,提出了一种改进的基于脊波变换的图像去噪方法;并将其应用于受不同强度高斯噪声污染的图像中,实验结果表明该方法对噪声具有良好的噪声拟制作用,能较好地保留图像的细节信息,去噪性能优于常用方法。     

一种小波和脊波联合去噪方法

由于小波变换不能有效地处理图像中的奇异线,而脊波变换能很好地弥补这一不足,提出了一种基于图像分块的小波和脊波联合去噪方法。该方法把噪声图像分成一定尺寸的图像块并选择其中的同质块和非同质块;利用小波去噪方法处理同质块,而非同质块用脊波去噪方法处理得到去噪后的图像;用维纳滤波器进一步处理去噪后的图像。实验表明,该方法与单纯的小波去噪方法和脊波去噪方法相比,信噪比有了较高的改善,能有效地保留图像的边缘细节信息。     

一种新的脊波变换方法

小波变换适用于表示具有各向同性奇异性对象的局部特性,脊波变换适用于表示具有各向异性奇异性对象的局部特性,但是各自对于对方所适用的局部特性的应用效果却不明显.提出了一种新的对脊波理论加以改进的多分辨分析方法--拟脊波多分辨分析方法.该方法统一了小波理论和脊波理论,使小波理论和脊渡理论成为该方法的两种特殊情形.同时它具有对各向同性和异性的奇异性对象的辨识能力.通过实验比较表明,该方法对小波理论和脊波理论优点的组合、缺点的规避相当明显,在图像去噪应用中具有更强的灵活性.     

基于曲波变换的图像去噪新算法

比较了小波变换和曲波变换,指出小波变换只具有点状奇异性的不足之处和曲波变换具有多尺度各向奇异性的优点,分析了现有的基于曲波变换的图像去噪方法,并对目前基于曲波变换的去噪算法进行了改进,提出结合Wrapping和Cycle Spinning的WCSCurvelet去噪新算法.仿真实验的结果证实了该算法减少了伪Gibbs现象,较好地保留了图像的细节和纹理,获得了更好的视觉效果和更高的峰值信噪比.     

基于曲波变换和小波变换的图像去噪算法

针对应用曲波变换进行图像处理过程中所产生的伪影现象,提出一种基于快速离散曲波变换和小波变换的联合去噪算法。对噪声图片分别采用非抽样小波变换和快速离散曲波变换进行去噪,并对经过快速离散曲波变换去噪后的图像进行四叉树分解,根据分解结果对图像进行重构得到最终融合图像。实验结果表明,联合去噪算法对图像的边缘和均匀区域都有较好的去噪效果,能够有效抑制伪影,具有较高的峰值信噪比。     

基于改进脊波变换的抗攻击数字水印算法

针对小波变换域不是图像轮廓边缘特征最佳表示的问题,在研究小波变换的基础上,利用脊波变换比小波变换更适合表示图像轮廓边缘特征的特性,结合人类视觉系统特性和奇偶量化算法改进脊波变换算法,提出了一种基于改进脊波变换的数字水印算法.将原始图像均匀分割,对各子块执行脊波变换,再结合人类视觉系统特性选择恰当的脊波系数,利用奇偶量化算法嵌入一维水印序列,最后作脊波反变换得到含水印图像.仿真实验结果表明,算法不仅具有很好的透明性,而且在抵抗攻击时表现出较好的鲁棒性.通过与小波变换算法的性能比较,本文算法在抗加噪、滤波和剪切的攻击中鲁棒性较强.     

基于正交有限脊波变换的图像压缩

对于纹理(线奇异性)丰富的图像,脊波可以获得比小波更加稀疏的表示。统计表明边缘表示了图像的主要信息。利用脊波对"线奇异性"图像的最优逼近的思想,设计出基于正交有限脊波变换的图像压缩算法。通过对图像的脊波系数进行量化和编码达到压缩图像的目的。实验结果表明,与基于小波的压缩算法相比,该算法能获得更高的压缩率,同时保持较高的峰值信噪比和良好的重建图像视觉效果。     

基于脊波变换的图像压缩

对于含有丰富边缘信息的图像,小波变换并不是很有效的表示方法,而脊波对线奇异性图像能够进行最优的逼近.利用脊波变换后的系数之间的相关性,结合SPIHT编码算法设计出基于脊波变换的图像压缩算法以达到压缩图像的目的.实验结果表明,该算法能获得高的压缩率,同时能保持较高的峰值信噪比和良好的重建图像视觉效果.     

一种基于脊波变换的图像去噪方法

Ridgelet是继小波变换(Wavelet)后提出的一种新型的多尺度分析方法,它特别适合于具有直线或超平面奇异性的二维信号的描述,而且具有较高的逼近精度.利用脊波变换的方法,设计了一种基于脊波变换的改进的图像去噪算法,该算法采用指数型阈值函数,利用SureShrink自适应阈值.试验结果表明,该方法比起Wavelet全局硬、软阈值,Ridgelet全局硬、软阈值,具有明显的优越性.     

基于单尺度脊波变换的阈值滤波方法

分析了高斯白噪声在单尺度脊波域中的统计性质，提出了一种新的基于单尺度脊波变换的阈值滤波算法。仿真结果表明，这种算法不仅比传统基于小波变换的各种滤波算法有更高的PSNR值，而且能更好地保持图像细节。     

A new digital implementation of ridgelet transform for images of dyadic length

Ridgelet transform is a new directional multi-resolution transform and it is more suitable for describing the signals with high dimensional singularities. Finite ridgelet transform is a discrete version of ridgelet transform, which is as numerical precision as the continuous ridgelet transform and has low computational complexity. However, finite ridgelet transform is only suitable for images of prime-pixels length, which is a limitation of its applications in image processing. In this paper, a new digital implementation of ridgelet transform that is suitable for images of dyadic length is proposed. This method not only expands the applications of finite ridgelet transform, but also simplifies the algorithm. First, we introduce the concept of ridgelet transform in the continuous domain. Then, we illustrate finite ridgelet transform and the new method. Finally, we compare the new method with finite ridgelet transform by applying both digital ridgelet transforms to the denoising of images embedded in additive white Gaussian noise and the new method gets a better performance in image denoising. The new algorithm can also be used as the important building block in curvelet transform and get surprising visual performances in denoising for natural image.     

基于脊波的多光谱和全色图像融合方法研究

应用了双线性插值的矩形阵列到径向阵列的变换算法,给出了一个离散脊波变换的实现方法,将其应用于多光谱图像与全色图像的融合算法中,通过清晰度、灰度方差、信息熵三个方面,将算法结果与小波变换的结果进行了对比,实验结果表明,相对于小波变换而言,脊波变换能更好地处理线和面的奇异性,而且由融合的结果来看,脊波变换得到的结果在清晰度等方面要高于小波变换。     

A 4-quadrant curvelet transform for denoising digital images

The conventional discrete wavelet transform (DWT) introduces artifacts during denoising of images containing smooth curves. Finite ridgelet transform (FRIT) solved this problem by mapping the curves in terms of small curved ridges. However, blind application of FRIT all over an image is computationally heavy. Finite curvelet transform (FCT) selectively applies FRIT only to the tiles containing small portions of a curve. In this work, a novel curvelet transform named as 4-quadrant finite curvelet transform (4QFCT) based on a new concept of 4-quadrant finite ridgelet transform (4QFRIT) has been proposed. An image is band pass filtered and the high frequency bands are divided into small non-overlapping square tiles. The 4QFRIT is applied to the tiles containing at least one curve element. Unlike FRIT, the 4QFRIT takes 4 sets of radon projections in all the 4 quadrants and then averages them in time and frequency domains after denoising. The proposed algorithm is extensively tested and benchmarked for denoising of images with Gaussian noise using mean squared error (MSE) and peak signal to noise ratio (PSNR). The results confirm that 4QFCT yields consistently better denoising performance quantitatively and visually.     

基于修正维纳滤波的小波包变换图像去噪

图像去噪是图像处理中一个非常重要的环节。为了改善降质图像质量,根据Donoho提出的小波阈值去噪算法,分析了维纳滤波原理,提出了一种基于修正维纳滤波的小波包变换图像去噪方法。利用修正维纳滤波对噪声图像进行处理,用处理后的图像计算噪声的标准方差,以此作为小波包的阈值。利用小波包对维纳滤波后的图像进行分解,实现对图像的低频和高频部分分别进行分解,用计算出的阈值对小波包树系数进行软阈值处理。利用小波包逆变换来获取去噪后的图像。结果表明：在噪声方差为0.01时,经该算法去噪后图像的PSNR比小波包自适应阈值去噪后的PSNR高出8.8 dB。该算法不仅能有效地去除加性高斯白噪声,而且能很好地保留边缘信息,极大地改善了图像的视觉质量。     

Adaptive digital ridgelet transform and its application in image denoising

In this paper, we propose a new multiscale decomposition algorithm called adaptive digital ridgelet (ADR) transform. Differently from the traditional nonadaptive multiscale decompositions, this algorithm can adaptively deal with line and curve information in an image by considering its underlying structure. As the key part of the adaptive analysis, the curve parts of an image are detected accurately by a new curve part detection method. ADR transform is applied to image denoising experiment in this paper. Experimental results demonstrate its efficiency for reducing noises as PSNR values can be improved maximally 5 dB compared with other methods and MAE values are also considerably improved. A new comparison criterion is also proposed and using this criterion, it is shown that ADR transform can provide a better performance in image denoising.     

Wrap-around effect removal finite ridgelet transform for multiscale image denoising

Denoising in wavelet domain is an attractive research field. Recently, the research focused on finding some algorithms that can detect line singularities of image while smoothing noise. Based on the finite Radon transform a finite ridgelet transform was derived for this purpose. But the wrap-around effect of the finite Radon transform limits its power greatly. In this paper, a way is pointed out to remove the wrap-around effect of the finite Radon transform. By embedding it into a moving window pyramid, a multiscale image denoising algorithm is developed.