Improvement of image quality of digital holographic reconstruction and development of related systems

The introduction of speckle noise in the process of digital holographic reconstruction is inevitable. Meanwhile, the quality of the reconstructed images are seriously lower than that of the original images, affecting the visual perception. The removal of speckle noise is an internationally recognized conundrum. Currently, scholars have not proposed a better method to remove speckle noise, which hinders the further development of digital holography technology. As a result, reducing speck noise in digital holographic reconstruction and enhancing the quality of reconstructed images have become important research topics. Based on the characteristics of speckle noise in reconstructed images, this paper proposes a new method for the first time by combining the concepts of image segmentation, guided filtering, and filter reconstruction, which can significantly improve the image quality within a reasonable time. By comparison with other state-of-the-art methods, the proposed method perform excellently in terms of detail preservation and background noise suppression of the target image. Finally, a holographic reconstruction image quality enhancement system is developed, integrating the physical experiment of digital holographic reconstruction with the image quality enhancement algorithm. The simple and convenient operation of the system provides great help for non-algorithm physics researchers. Additionally, it is also the first holographic reconstruction image enhancement system with a good effect and has considerable market application value.

     

小波与双边滤波的医学超声图像去噪

目的：医学超声图像中的斑点噪声降低了图像质量并且限制了超声图像自动化诊断技术的发展。针对斑点噪声问题,提出了一种新型的基于小波和双边滤波的去噪算法。方法：首先,根据医学超声图像在小波域内的统计特性,在通用小波阈值函数的基础之上,改进了小波阈值函数。其次,将无噪信号的小波系数和斑点噪声的小波系数分别建模为广义拉普拉斯分布模型和高斯分布模型,利用贝叶斯最大后验估计方法得到了新型的小波收缩算法,利用小波阈值法对小波域内的高频信号分量进行去噪。最后,对小波域内的低频信号分量进行双边滤波处理,然后利用小波逆变换便得到去噪后的图像。结果：在仿真实验中,通过与其它7种去噪算法作对比,观察峰值信噪比(PSNR)等图像质量评价指标,结果表明本文算法的去噪效果优于其他相关算法。临床超声图像的实验结果进一步验证了本文算法的去噪性能。结论：本文提出了一种新型的去噪算法,实验表明本文算法能够很好地抑制斑点噪声,并且能保留图像病灶边缘等细节。     

引入欧氏距离的各向异性扩散相干斑抑制

目的 相干斑噪声严重影响SAR影像解译。抑制相干斑同时,获取较好的边缘保持效果始终是相干斑抑制的重点。针对该问题,提出一种引入欧氏距离的各向异性扩散（EDAD）相干斑抑制方法。方法 EDAD算法以P-M模型与SRAD算法为基础,利用邻近像素间区域欧氏距离代替原有边缘检测算子,自适应区分同质区与异质区,有效构造各向异性扩散系数,完成相干斑抑制。结果 运用EDAD算法与现存各向异性扩散算法对截取的两景TanDEM-X影像进行试验研究并比较各类算法的评估参数。EDAD算法的等效视数分别为3.996与5.859,均高于其他算法,体现优越的相干斑抑制能力;EDAD算法相干斑抑制前后比值影像的均值分别为0.999与1.001,方差分别为0.270与0.269,较其他算法均更接近理想值1与0.273,展现更优边缘保持与相干斑抑制能力。结论 本文算法可有效提高边缘检测能力,获取更优相干斑抑制效果。经验证,对分布较散的弱相干斑区域与分布较集中的强相干斑区域均有较好适用性。     

A disk expansion segmentation method for ultrasonic breast lesions

Automatically extracting lesion boundaries in ultrasound images is difficult due to the variance in shape and interference from speckle noise. An effective scheme of removing speckle noise can facilitate the segmentation of ultrasonic breast lesions, which can be performed with an iterative disk expansion method. In this study, a disk expansion segmentation method is proposed to semi-automatically find lesion contours in ultrasonic breast image. To evaluate the performance of the proposed method, the simulations with seven types of cysts, three in vitro phantom images and 10 clinical breast images are introduced. The mean normalized true positive area overlap between simulated contours and contours obtained by the proposed method is over 85% in simulation results. A strong correlation exists between physicians’ manual delineations and detected contours in clinical breast images. In addition, the method is also verified to be able to simultaneously contour multiple lesions in a single image. In comparison with the conventional active contour model, our proposed method does not require any initial seed within a lesion and thus, it is more convenient and applicable.     

改进权值非局部均值超声图像去噪

目的 超声图像斑点噪声会影响诊断的准确性和可靠性。通过分析超声图像斑点噪声统计模型,结合非局部均值滤波算法,提出一种基于超声斑点噪声模型的改进权值非局部均值（NLM）滤波算法。方法 算法针对超声图像灰度信息对图像进行预处理,利用超声图像斑点噪声模型改进传统NLM算法的权值计算函数,基于图像特征确定最优采样间隔进行下采样,利用改进后的权值计算函数对图像进行NLM去噪处理。结果 分别采用人工合成与真实超声图像对本文算法性能进行测试,并与传统非局部均值滤波算法、非局部总变分（NLTV）等算法进行去噪效果比较,同时采用均方误差、峰值信噪比和平均结构相似性作为滤波算法性能的客观评价指标。本文算法能快速完成超声图像的去噪处理,峰值信噪比较其他算法可以提高0.2 dB以上,可以降低均方误差,提高平均结构相似性,缩短处理时间,并得到较好的图像质量和视觉效果。结论 根据超声图像斑点噪声模型对NLM算法的权值计算函数进行优化,使得NLM图像滤波算法能更好地适用于超声图像的去噪,基于超声斑点噪声模型的改进权值NLM算法相较于其他算法,滤波效果更佳,适合超声图像去噪。     

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。     

基于深度协同稀疏编码网络的海洋浮筏SAR图像目标识别

浮筏养殖广泛存在于我国近海海域, 可见光遥感图像无法完全准确地获取养殖目标, 而基于主动成像的合成孔径雷达(Synthetic aperture radar, SAR)遥感图像能够得到养殖目标, 因此采用SAR图像进行海洋浮筏养殖目标识别. 然而, 海洋遥感SAR图像包含大量相干斑噪声, 并且SAR图像特征单一, 使得目标识别难度较大. 为解决这些问题, 提出一种深度协同稀疏编码网络(Deep collaborative sparse coding network, DCSCN)进行海洋浮筏识别. 本文方法对预处理后的图像先提取纹理特征和轮廓特征, 再进行超像素分割并将同一个超像素块特征组输入该网络进行协同表示, 最后得到有效特征并分类识别. 通过人工SAR图像和北戴河海域浮筏养殖SAR图像的实验验证所提模型的有效性. 该网络不仅具有优异的特征表示能力, 能够获得更适合分类器的特征, 而且通过近邻协同约束, 有效抑制相干斑噪声影响, 所以提高了SAR图像目标识别精度.     

SAR despeckling neural network with logarithmic convolutional product model

ABSTRACT

Synthetic aperture radar (SAR) images are inevitably contaminated by speckle noise due to its coherent imaging mechanism. Speckle noise obscures the intrinsic radar cross section (RCS) information in SAR images. This article proposes a novel deep neural network architecture specifically designed for despeckling purpose. It uses a convolutional neural network to extract image features and reconstruct a discrete RCS probability density function (PDF). It is trained by a hybrid loss function which measures the distance between the actual SAR image intensity PDF and the estimated one which is derived from convolution between the reconstructed RCS PDF and prior speckle PDF. The network can be trained by either purely simulated image patches or real SAR images. Experiment results on both simulated SAR images and real NASA/JPL AIRSAR images are used to test the performance, and the results show the efficacy of the proposed despeckling neural network compared with three state-of-the-art filters.     

Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method

An ultrasound speckle reduction method is proposed in this paper. The filter, which enhances the power of anisotropic diffusion with the Smallest Univalue Segment Assimilating Nucleus (SUSAN) edge detector, is referred to as the SUSAN-controlled anisotropic diffusion (SUSAN_AD). The SUSAN edge detector finds image features by using local information from a pseudo-global perspective. Thanks to the noise insensitivity and structure preservation properties of SUSAN, a better control can be provided to the subsequent diffusion process. To enhance the adaptability of the SUSAN_AD, the parameters of the SUSAN edge detector are calculated based on the statistics of a fully formed speckle (FFS) region. Different FFS estimation schemes are proposed for envelope-detected speckle images and log-compressed ultrasonic images. Adaptive diffusion threshold estimation and automatic diffusion termination criterion are employed to enhance the robustness of the method. Both synthetic and real ultrasound images are used to evaluate the proposed method. The performance of the SUSAN_AD is compared with four other existing speckle reduction methods. It is shown that the proposed method is superior to other methods in both noise reduction and detail preservation.     

一种对Gamma分布的SAR图像相干斑去噪方法

鉴于Gamma分布的SAR图像相干斑经对数变换后可近似为高斯分布,提出一种基于粒子群优化的BP神经网络复原去噪算法。首先用高斯噪声对无噪图像进行模糊处理,然后将结果和原图像组成训练对,用于训练优化后的神经网络,最后利用训练好的神经网络对SAR图像进行复原,从而达到去除相干斑的目的。实验表明,该算法能有效解决传统去噪算法在图像失真、边缘模糊方面的问题,收敛速度快,迭代次数少,归一化均方误差(NMSE)和峰值噪比(PSNR)效果更好。     

多区域SAR图像分割的改进水平集方法

由于合成孔径雷达（SAR）图像易受相干斑噪声的影响,光学图像的分割方法并不适用于SAR图像,更不能获得精确的分割结果对比,因此,首先基于GA^0统计模型定义能量映射函数以代替像素值进行后续处理,减小相干斑的影响;其次,使用水平集算法对处理后的图像进行分割处理,选用了一种形式更为简单的水平集函数,并可以较容易地推广到多区域SAR图像分割情况。实验结果表明,该方法可以减少相干斑噪声对SAR图像分割过程的不良影响,具有较好的准确性。     

基于卡尔曼滤波的SAR图像边缘检测方法

针对传统 Canny 边缘检测算法对合成孔径雷达(SAR)图像的相干斑噪声抑制程度 太高,导致大量边缘的真实信息丢失问题,提出一种新型 Canny 算子边缘检测算法。首先建立 合适的非对称半平面区域(NSHP)图像模型,将空间模型转换成卡尔曼滤波可适用的系统状态方 程;然后用“预测+反馈”的方式对图像去噪;最后通过双阈值算法提取图像的边缘。仿真实验表 明,该方法可以有效地抑制 SAR 图像中的相干斑噪声,同时能较好地保留图像的边缘信息,相 对于传统的 Canny 算法有较好的检测效果。     

改进卷积神经网络SAR图像去噪算法

合成孔径雷达（SAR）通常会被一种称为散斑的乘性噪声干扰，这使得图像的解释变得困难。为解决这一问题，提出一种改进卷积神经网络SAR图像去噪方法。对图像进行下采样再对下采样子图像进行卷积提取特征，这可以有效扩大感受野提高去噪效率；为了减少梯度消失问题和提高模型去噪性能，网络又引入了跳跃连接和残差学习策略；利用仿真和实测数据对网络进行测试与评估，实验结果表明提出的方法具有良好的去噪效果和较高的计算效率，对比其他去噪方法，该方法不仅去噪效果好，而且效率更高。     

Noise reduction in synthetic aperture radar images using fuzzy logic and genetic algorithm

Synthetic aperture radar (SAR) is a self-illuminating imaging technique; it produces high resolution images in all weather conditions, day and night. SAR images are widely accepted and used by many application scientists. However, the SAR images are corrupted with speckle noise. Speckle noises are caused by random interference of electromagnetic signals scattered by the object surface within one resolution element. The amount of noise and distribution of noise corrupting the image is unpredictable. Conventional noise filters are quantitative in nature; they are not well suited for uncertainty problems. Fuzzy logic is capable of handling uncertainty. In this work, noisy pixels in the images are identified by using fuzzy rules and filtered using fuzzy weighted mean, keeping the healthy pixels unchanged. The optimum value of parameters used in defining fuzzy membership function is determined by using genetic algorithm (GA). Reducing noise and simultaneously preserving image details are the two most desirable characteristics of noise filters. Peak signal-to-noise ratio (PSNR) and edge preserving factor (EPF) are used to evaluate the performance of the proposed fuzzy filter. SAR images affected by varying amounts of speckle noise are used to evaluate the performance. It was observed that the proposed filter suppresses noise and preserves image edges.

     

基于模糊连接度的高分辨率SAR图像道路自动提取

针对高分辨率合成孔径雷达(SAR)图像受到乘性斑点噪声的影响,且道路环境复杂多变的问题,提出一种基于模糊连接度的高分辨率SAR图像道路自动提取方法。首先,对SAR图像进行斑点滤波,以降低斑点噪声的影响;其次,结合指数加权均值比(ROEWA)算子检测结果和模糊C均值(FCM)分割结果自动提取种子点,从而提高自动化程度;最后,利用以图像灰度和ROEWA检测算子边缘强度为特征的模糊连接度算法对种子点进行扩展提取道路,经形态学处理后得到最终结果。对两幅SAR图像进行实验,并与FCM方法分割出的道路结果进行比较,所提出的方法在提取完整率、正确率及检测质量上均优于模糊C均值方法。实验结果表明,所提出的方法能较有效地从高分辨率SAR图像中提取不同宽度和弯曲程度的道路,且无需人工输入种子点。     

基于小波域空间树结构的合成孔径雷达图像压缩

空间树结构SOT(Spatial-Orientation Tree)在基于小波的SAR图像压缩中扮演着及其重要的角色,包括EZW(Embedded Zero-tree Wavelet)和SPIHT(Set Partitioning in Hierarchical Trees)的图像压缩编码方法,都利用了SOT中的父子关系。斑点噪声的存在,严重降低了SAR图像的质量和可压缩性。作为研究不同分辨率小波系数的空间相关性的非常有效的数据结构,SOT在斑点噪声去除中并没有得到很好的利用。提出一种新的SAR图像压缩方法,该方法结合基于SOT结构的斑点噪声去除和EZW嵌入式零树编码算法,对机载合成孔径雷达图像压缩实验的结果显示,该方法优于JPEG和标准EZW算法。     

一种基于小波域隐马尔可夫模型的SAR相干斑抑制算法

相干斑噪声是SAR图像的固有特点。对相干斑抑制的要求是在平滑噪声的同时，尽量保持原始图像的结构信息。现有的许多相干斑抑制方法各有优点和不足，没有普遍的适用性。基于图像在小波域的隐马尔可夫模型(HMMs)结构，结合SAR图像中相干斑噪声的统计特性，本文提出了一种新的小波域相干斑抑制方法。仿真及实测数据处理结果表明，该方法在有效抑制相干斑的同时，更好地保持了边缘结构。与小波域软阈值去噪方法和Lee滤波器相比较，该方法在噪声平滑及边缘保持上都取得了较大的改进，并得到了较好的视觉效果。     

量子衍生PDE医学超声图像去斑

目的 医学超声图像常常受到斑点噪声的污染而导致质量降低,影响后续诊疗.为了解决医学超声图像在滤波去斑的同时保持图像边缘细节和结构特征的问题,借鉴量子力学的基础理论,提出一种量子衍生偏微分方程(PDE)医学超声图像去斑方法.方法 针对传统P-M方程各向异性扩散的自适应去斑能力有限的问题,引入量子理论改进扩散系数增强去斑算法的自适应能力.同时构造出各向异性扩散模型,提出一种量子衍生的偏微分方程医学超声图像去斑方法.结果 通过对模拟斑点噪声污染的图像和真实医学超声图像实验,比较信噪比(SNR)、边缘保持度、结构相似度(SSIM)等客观评价指标,本文方法较其他图像去斑方法更能有效去除斑点噪声,同时又能较好地保持图像边缘细节与结构特征.结论 本文方法能够有效地解决医学超声图像去斑中保持图像细节特征的问题,同时,量子理论的引入也为后续医学超声图像的研究提供了新思路.     

基于偏微分方程的医学超声图像去噪方法

研究了各向异性扩散方程在医学超声图像去噪中的应用。在理论上对去噪原理进行了分析,并在此基础上采用改进的针对乘性噪声的各向异性扩散算法对医学超声图像去噪,实验结果表明,该方法在有效去除噪声的同时较好地保留了医学超声图像中的重要细节信息,使图像的细节部分清晰。该方法可以有效地去除超声图像斑纹噪声,提高图像的质量。     

Fractional subpixel diffusion and fuzzy logic approach for ultrasound speckle reduction

Speckle is the dominant source of noise in ultrasound imaging and is a kind of multiplicative noise. It is difficult to design a filter to remove speckle effectively. In this paper, a novel fuzzy subpixel fractional partial difference (FSFPD) for ultrasound speckle reduction is proposed. Euler-Lagrange equation acts as an increasing function of the fractional derivative's absolute value of the image intensity function. The fractional order partial difference is computed in the frequency and fuzzy domain with subpixel precision. We test the proposed method on both synthetic and real breast ultrasound (BUS) images. The comparisons of the experimental results show that the proposed method can preserve edges and structural details of ultrasound images well while removing speckle noise. In addition, the filtered images are assessed and evaluated by radiologists using double blind method. The results demonstrate that the discrimination rate of breast cancers has been highly improved after employing the proposed method.