Multi-focus image fusion based on block matching in 3D transform domain

Fusion methods based on multi-scale transforms have become the mainstream of the pixel-level image fusion. However,most of these methods cannot fully exploit spatial domain information of source images, which lead to the degradation of image.This paper presents a fusion framework based on block-matching and 3D(BM3D) multi-scale transform. The algorithm first divides the image into different blocks and groups these 2D image blocks into 3D arrays by their similarity. Then it uses a 3D transform which consists of a 2D multi-scale and a 1D transform to transfer the arrays into transform coefficients, and then the obtained low-and high-coefficients are fused by different fusion rules. The final fused image is obtained from a series of fused 3D image block groups after the inverse transform by using an aggregation process. In the experimental part, we comparatively analyze some existing algorithms and the using of different transforms, e.g. non-subsampled Contourlet transform(NSCT), non-subsampled Shearlet transform(NSST), in the 3D transform step. Experimental results show that the proposed fusion framework can not only improve subjective visual effect, but also obtain better objective evaluation criteria than state-of-the-art methods.

3D Filtering by Block Matching and Convolutional Neural Network for Image Denoising

Block matching based 3D filtering methods have achieved great success in image denoising tasks. However the manually set filtering operation could not well describe a good model to transform noisy images to clean images. In this paper, we introduce convolutional neural network （CNN） for the 3D filtering step to learn a well fitted model for denoising. With a trainable model, prior knowledge is utilized for better mapping from noisy images to clean images. This block matching and CNN joint model （BMCNN） could denoise images with different sizes and different noise intensity well, especially images with high noise levels. The experimental results demonstrate that among all competing methods, this method achieves the highest peak signal to noise ratio （PSNR） when denoising images with high noise levels （σ 〉 40）, and the best visual quality when denoising images with all the tested noise levels.

Image Restoration Using Hybrid Features Improvement on Morphological Component Analysis

Images are generally corrupted by impulse noise during acquisition and transmission.Noise deteriorates the quality of images.To remove corruption noise,we propose a hybrid approach to restoring a random noisecorrupted image,including a block matching 3D(BM3D)method,an adaptive non-local mean(ANLM)scheme,and the K-singular value decomposition(K-SVD)algorithm.In the proposed method,we employ the morphological component analysis(MCA)to decompose an image into the texture,structure,and edge parts.Then,the BM3D method,ANLM scheme,and K-SVD algorithm are utilized to eliminate noise in the texture,structure,and edge parts of the image,respectively.Experimental results show that the proposed approach can effectively remove interference random noise in different parts;meanwhile,the deteriorated image is able to be reconstructed well.

基于显著性检测的声呐图像快速降噪研究

声呐图像在获取过程中易受噪声污染,而降噪性能好的算法通常时间复杂度较高。鉴于人类视觉注意机制,将基于流形排序(MR)的显著性检测方法引入声呐图像处理,将图像自动分割为显著区域和非显著区域两部分。对于占比小的显著区域采用三维块匹配(BM3D)算法降噪以保护图像主要信息,对非显著背景区域采用执行效率较高的均值滤波(MF)算法。将所提算法同经典MF,BM3D算法进行主观和客观评价指标对比,结果表明,所提算法在提高图像视觉效果的同时,执行时间较BM3D算法大为减少,可以满足水下航行器实时作业的应用需求。

改进的Roberts图像边缘检测算法

针对复杂背景下图像边缘检测中存在抗噪性能不强、边缘不连续等问题,提出了改进的Roberts边缘检测算法。该算法采用3×3邻域代替Roberts算法中2×2邻域来计算梯度幅值;并利用图像块之间相似性的三维块匹配的去噪模型,提高Roberts算子的检测精度和抗噪性能;通过最佳阈值迭代方法代替人为指定阈值来获取最佳分割阈值,有效地提取图中目标轮廓。仿真实验结果表明,该算法PSNR达到33dB左右,比抗噪形态学边缘检测算法和一种改进的Roberts和灰色关联分析的边缘检测算法抗噪性能好,在抑制噪声干扰的同时,能保留边缘信息,较好提取目标的整体轮廓信息,为后续目标识别奠定基础。

帧率提升中的三维递归搜索块匹配改进算法

将基于块匹配的运动估计用于帧率提升会产生内插重叠、空洞等问题。对三维递归搜索(3DRS)块匹配运动估计算法进行改进,结合双向运动估计给出更为精确的运动矢量,采用矢量中值滤波器平滑矢量场,并运用中值滤波运动补偿技术进行插帧。实验结果表明,该算法在主客观方面都具有较好的性能,计算复杂度较低,也便于硬件实现。

基于三维块匹配与改进Top-hat的红外图像目标检测方法

针对复杂背景干扰下难以精确检测出红外图像目标的问题,提出基于三维块匹配(BM3D)与改进顶帽(Top-hat)的红外图像目标检测方法。该方法首先采用三维块匹配算法对红外图像进行滤波,更好地保留图像的边缘信息;其次构建改进Top-hat算子,利用不同大小、不同形状的结构元素对滤波后图像进行背景估计,得到校正后图像;最后对校正后图像进行阈值分割,得到目标图像。仿真实验结果表明,与经典Top-hat算法比较,提出的方法能够有效地增强红外图像对比度、抑制噪声干扰、减弱非均匀加热背景的影响,从而突出红外图像目标信息,使得红外图像目标检测更加准确。

射电日像仪的压缩感知和脏图高斯去噪

传统的综合孔径射电日像仪成像原理是根据香农采样定理,使用香农采样得到完整的频谱数据,进行反傅里叶变换得到图像.因成像设备及外界环境因素,频谱数据中伴随有大量非真实信号数据,导致所成图像产生大量噪声,通常称为脏图.射电天文领域通常采用相关Clean算法处理脏图,得到“干净”的射电图像.为了降低了射电信号的采样成本,而且能得到更“干净”的射电图像,基于射电干涉稀疏成像与压缩感知理论,实现了从不完整的频谱中重建脏图,之后进行噪声去除:采用正交跟踪匹配与特征标志算法完成从稀疏频谱中重建脏图,之后使用三维块匹配算法去除噪声.

局部均值噪声估计的盲3维滤波降噪算法

目的图像在获取和传输的过程中很容易受到噪声的干扰,图像降噪作为众多图像处理系统的预处理模块在过去数十年中得到了广泛的研究。在已提出的降噪算法中,往往采用加性高斯白噪声模型AWGN(additive white Gaussian noise)为噪声建模,噪声水平(严重程度)由方差参数控制。经典的BM3D 3维滤波算法属于非盲降噪(non-blind denoising algorithm)算法,在实际使用中需要由人工评估图像噪声水平并设置参数,存在着噪声评估值随机性大而导致无法获得最佳降噪效果的问题。为此,提出了一种新的局部均值噪声估计(LME)算法并作为BM3D算法的前置预处理模块。方法本文专注于利用基于自然统计规律(NSS)的图像质量感知特征和局部均值估计技术构建图像噪声水平预测器,并通过它高效地获得噪声图像中准确的噪声水平值。关于自然场景统计方面的研究表明,无失真的自然场景图像在空域或者频率域上具有显著的统计规律,一旦受到噪声干扰会产生规律性的偏移,可以提取这些特征值作为反映图像质量好坏的图像质量感知特征。另外,局部均值估计因其简单而高效率的预测特性被采用。具体实现上,在具有广泛代表性且未受噪声干扰图像集合上添加不同噪声水平的高斯噪声构建失真图像集合,然后利用小波变换对这些失真图像进行不同尺度和不同方向的分解,再用广义高斯分布模型(GGD)提取子带滤波系数的统计信息构成描述图像失真程度的特征矢量,最后用每幅失真图像上所提取的特征矢量及对其所施加的高斯噪声水平值构成了失真特征矢量库。在降噪阶段,用相同的特征提取方法提取待降噪的图像的特征矢量并在失真特征矢量库中检索出与之类似的若干特征矢量及它们所对应的噪声水平值,然后用局部均值法估计出待降噪图像中高斯噪声大小作为经典BM3D算法的输入参数。结果改进后的BM3D算法转换为盲降噪算法,称为BM3D-LME(block-matching and 3D filtering based on local means estimation)算法。准确的噪声估计对于诸如图像降噪,图像超分辨率和图像分割等图像处理任务非常重要。已经验证了所提出噪声水平估计算法的准确性、鲁棒性和有效性。结论相对人工进行噪声估计,LME算法能够准确、快速地估算出任意待降噪图像中的噪声大小。配合BM3D算法使用后,有效提高了它的实际降噪效果并扩大它的应用范围。

Improved image denoising via RAISR with fewer filters

In recent years,accurate Gaussian noise removal has attracted considerable attention for mobile applications,as in smart phones.Accurate conventional denoising methods have the potential ability to improve denoising performance with no additional time.Therefore,we propose a rapid post-processing method for Gaussian noise removal in this paper.Block matching and 3D filtering and weighted nuclear norm minimization are utilized to suppress noise.Although these nonlocal image denoising methods have quantitatively high performance,some fine image details are lacking due to the loss of high frequency information.To tackle this problem,an improvement to the pioneering RAISR approach(rapid and accurate image super-resolution),is applied to rapidly post-process the denoised image.It gives performance comparable to state-of-the-art super-resolution techniques at low computational cost,preserving important image structures well.Our modification is to reduce the hash classes for the patches extracted from the denoised image and the pixels from the ground truth to 18 filters by two improvements:geometric conversion and reduction of the strength classes.In addition,following RAISR,the census transform is exploited by blending the image processed by noise removal methods with the filtered one to achieve artifact-free results.Experimental results demonstrate that higher quality and more pleasant visual results can be achieved than by other methods,efficiently and with low memory requirements.

江陵凹陷三维连片地震资料一致性处理实践

江陵凹陷是良好产油区,李家台地区连续勘探了多块三维地震资料,受复杂地表地震条件和施工因素影响,各区块间存在地震资料特征不一致问题。首先要提高原始地震资料信噪比,采用互相关分析、地表一致能量补偿和反褶积、匹配滤波等方法进行连片一致性处理,使各区块间信号特征达到一致,再利用傅里叶变换重构缺失信号,将面元属性均化,处理得到的地震剖面中深层同相轴连续性较好,目的层新沟咀组可连续追踪,断坳结构清晰。