Video de-noising method based on 3D wavelet transform and block context model

考虑视频图像序列的各帧之间具有较强的相关性,提出了一种基于三维小波变换和分块COnTEXT模型的视频去噪新方法(3dWTbCM)。3dWTbCM法基于视频图像三维小波分解域内系数和噪声分布的特征,利用小波系数具有局部相关性对其进行分块,将系数分解成各个局部区域。然后,将COnTEXT模型用于局部块中,按照能量分布将块内的小波系数分成多个子块。对各部分进行能量估计和多阈值估计,获得去噪最佳阈值,并有效地消除噪声。实验结果表明,3dWTbCM的噪声抑制效果明显优于各种2d去噪方法和其他常用的3d去噪声方法,PSnr平均提高0.5~1.2db。而且从视觉效果来看,本文算法在去除噪声的同时,较好地保留了运动图像细节,运动物体显得比较平滑,不存在传统算法中的拖影、闪烁等现象。A video de-noising method based on the 3D Wavelet Transform and Block Context Model(3DWTBCM) is proposed according to the strong correlation between the two frames of video sequence.On the basis of the characteristics of the coefficients in 3D wavelet domain and noise distribution, wavelet coefficients are partitioned into subblocks firstly in the light of local relativity of these coefficients and then the Context model is used in the corresponding subblocks.The wavelet coefficients in each block are divided into several parts by means of their energy distribution in the 3D Context model and each part is estimated by its independent energy distribution.Finally, suitable thresholds are obtained.Experimental results show that 3DWTBCM achieves better de-noising performance than hierarchical 2D de-noising methods and its PSNR is improved more than 0.5-1.2 dB on average in comparison with those of common 3D de-noising methods.In terms of visual quality, 3DWTBCM can effectively preserve the video detail while de-noising the wavelet coefficients and especially can provide video frames with rapid movements and more textures.航空科学基金资助项目(No.05F07001);国家自然科学基金资助项目(No.60472081

Compression of Medical Image from SPIHI Algorithm

医学图像是医学诊断和疾病治疗的重要根据。为了实现图像的存储和远程医疗中快速传输图像的要求,必须对图像进行压缩。先分析CT医学图像经过小波变换后系数的统计特性,在基于小波变换的基础上,用SPIHT算法对CT医学图像进行压缩编码。提出了2种针对CT医学图像压缩编码改进的SPIHT算法。一是细化扫描产生的有效值,二是将小波变换后系数的低频近似部分按二进比特进行传输。用Matlab进行仿真,仿真结果表明改善了峰值信噪比。Medical images are important gist for medicinal diagnoses and disease treatments.In order to achieve the requirement of storing and long-distance transmitting images rapidly,medical image should be compressed.In this paper,the statistical properties of the coefficient of the CT medical image in wavelet transform domain are analyzed.Basing on the wavelet transform,the CT medical(images)are compressed by SPIHT algorithm.Two improved SPIHT algorithms are proposed in this paper.One improvement is to refine the effective scanning value,the other is to transfer the low-frequency approximate part of the wavelet coefficients in the form of(binary) bit.It is proved that the PSNR of the compressed images has been improved according to the simulation result by Matlab language

超小波分析及应用

尽管小波变换在数据压缩和去噪声等领域取得良好的效果，可分离的二维小波变换（不是直接构造出），采用先对行做一次一维小波变换，再对列做一次一维小波变换扩展而来。或者直接用二个可分离的一维函数基直接构造的二维变换，从数学角度都不是真正的二维函数。基函数的支撑区域由区间扩展为正方形，基函数形状的方向性较差,该问题制约着小波变换的进一步应用。同时，由于采用亚抽样技术，在目标提取时会造成信息模糊，对信息利用会产生较大的影响。众所周知，如果某个基函数能与被逼近的函数较好地匹配，则其相应的投影系数较大，变换的能量集中度较高。可见对于平滑区域，小波变换的表示效率较高，而对于图像中方向性较强的边缘以及纹理，由于两者匹配较差，导致其表示效率欠佳。在高维情况下,小波分析并不能充分利用数据本身特有的几何特征,并不是最优的或 “最稀疏”的函数表示方法。 多尺度几何发展的目的和动力正是要致力于发展一种新的高维函数的最优表示方法。 为克服小波分析的缺点，人们一直找其改进的方法。我们将这些方法统称超小波分析方法(Beyond Wavelet)。提到超小波分析，首先进行定义超小波分析。超小波分析就是把近来人们为改变小波分析的不足，提出常用基于小技术基础之上的系列变换，即Curvelet、Ridgelet、Contourlet、Bandelet、Beamlet、Directionlet、Wedgelet和Surfacelet变换的统称，也有人称X-let(包括Wavelet)。国家自然科学基金(No.60472081)和航空基础科学基金（No.05F07001)资

SAR image despeckling using nonsubsampled Contourlet transform

中文摘要: 提出了一种基于无下采样 Cont ourlet变换 (NSCT)的合成孔径雷达 ( S AR)图像去噪方法。首次在理论上证实了 S AR图像取对数后无下采样 Cont ourlet系数服从广义高斯分布 ,从而提出采用贝叶斯阈值方法估计不含噪声的无下采样 Cont ourlet系数 ,达到去除噪声的目的。仿真和实际实验结果表明 ,该方法在噪声平滑、 边缘和纹理保护等方面优于其他方法。由于无下采样 Cont ourlet变换不进行下采样 ,该方法能够避免其他进行严格下采样的变换去噪时所引入的人工痕迹。 英文摘要：Abstract: A nonsubsamp led Cont ourlet transform (NSCT) based des peckling method for synthetic aperture radar ( SAR) i mages is p resented . It is derived in theory for the first ti me that the coefficients of l ogtransfor med SAR images decomposed by NSCT obey general Gaussian distributi on, so Bayesian shrinkage fact or is adop ted t o esti mate noise free NSCT coefficients . Simulati on and experi ments demonstrate that the visual quality of the results is superi or to other des peckling methods in terms of both backgr ound s moothing, preservati on of edge sharpness and texture .The absence of deci mati on in Cont ourlet decompositi on avoids artificial i mpair ments often intr oduced by other critically subsamp led transfor m methods .基金项目:国家自然科学基金 (10605019)、 福建省自然科学基金 (2006J0227)、 厦门大学 985二期信息创新平台 ( 00002 X07204)、 厦门大学科技创新项目资

基于Surfacelet 变换的3D Context 模型视频去噪新方法

本文将3D Context 模型应用于Surfacelet 变换域,提出一种新的视频去噪方法. Surfacelet 变换(ST) 是一种新的3D 变换,具有多方向分解、各向异性和低冗余度等性质. 根据视频信号ST 域内系数和噪声分布的特征,将2D Context 模型拓展到3D ,按照能量分布将ST系数分成多个子块,每个子块有独立的能量和阈值估计. 实验结果表明,本文算法噪声抑制效果明显优于分层2D 去噪声方法和其它现有的3D 方法,去噪视频的PSNR 值提高了约2dB. 从视觉效果来看,本文算法在去除噪声的同时,能很好的保留视频图像细节,运动物体非常平滑,有效解决传统算法中存在的拖影、闪烁等问题,尤其适合于包含剧烈运动和丰富纹理图像的视频. Title: A Novel Video Denoising Method with 3D Context Model Based on Surfacelet Transform 　Abstract : 　We propose a novel video denoising method with 3D Context Model in Surfacelet Transform Domain (3DCMST) in this paper. In order to take advantage of the characteristic of the coefficients ,the Context model was extended from 2D to 3D. The ST coefficients were divided into several parts according to their energy distribution by 3D Context model and each part had independent energy estimate and threshold. Experimental results show that the proposed method achieves better denoising performance than other 3D or hierarchical 2D denoising methods , and remarkably improves the PSNR of video about 2dB. In terms of visual quality ,the proposed method can effectively preserve the video detail ,and the trajectory of motion object is very smooth ,which is especially adequate to process the video frames with acute movement and plenty of texture. Key words : 　video denoising ;Surfacelet transform;3D Context model ; directional filter bank国家自然科学基金(No. 60472081) ;航空科学基金(No. 05F07001

Research on human visualization

人体可视化是指运用计算机图形学和图形处理技术,将抽象的人体数据转换为图形及图像在屏幕上显示出来并进行交互处理的理论、方法和技术。目前的许多应用已经显示出它在医学领域的巨大发展潜力。主要介绍人体可视化的研究背景、常用的可视化开发工具包、图像分割在医学可视化中的作用,最后对它的应用发展前景进行展望。Human visualization is a kind of theory,method and technology,which applies computer graphics and image processing technology to convert nonobjective human data to graphics and image to display on the screen and take mutual processing.The applications of human visualization reveal its great potential in medical field,In this paper,the research background of the human visualization in medical is introduced,the common use of visualization toolkits are summarized,the effect of image segmentation in the medical visualization is also concluded.Finally,the prospect of its applications in medical field is discussed

A Novel Video Denoising Method with 3D Context Model Based on Surfacelet Transform

本文将3D Context模型应用于Surfacelet变换域,提出一种新的视频去噪方法.Surfacelet变换(ST)是一种新的3D变换,具有多方向分解、各向异性和低冗余度等性质.根据视频信号ST域内系数和噪声分布的特征,将2DContext模型拓展到3D,按照能量分布将ST系数分成多个子块,每个子块有独立的能量和阈值估计.实验结果表明,本文算法噪声抑制效果明显优于分层2D去噪声方法和其它现有的3D方法,去噪视频的PSNR值提高了约2dB.从视觉效果来看,本文算法在去除噪声的同时,能很好的保留视频图像细节,运动物体非常平滑,有效解决传统算法中存在的拖影、闪烁等问题,尤其适合于包含剧烈运动和丰富纹理图像的视频.We propose a novel video denoising method with 3D Context Model in Surfacelet Transform Domain(3DCMST)in this paper.In order to take advantage of the characteristic of the coefficients,the Context model was extended from 2D to 3D.The ST coefficients were divided into several parts according to their energy distribution by 3D Context model and each part had independent energy estimate and threshold.Experimental results show that the proposed method achieves better denoising performance than other 3D or hierarchical 2D denoising methods,and remarkably improves the PSNR of video about 2dB.In terms of visual quality,the proposed method can effectively preserve the video detail,and the trajectory of motion object is very smooth,which is especially adequate to process the video frames with acute movement and plenty of texture.国家自然科学基金(No.60472081);; 航空科学基金(No.05F07001

New Image Interpolation Method Based on Ramp Edge Model

基于斜坡边缘模型的经典插值方法把所有边缘归为强边缘,导致弱边缘过分增强而失真。针对该问题提出基于斜坡边缘模型的图像插值新方法(nIIbrEd),对强弱边缘采用不同方法,考虑边缘宽度随图像放大而增大的情况,对放大图像进行修复。实验结果证明,nIIbrEd使放大图像的边缘更自然且清晰,取得了更好的纹理效果。Classical interpolation method based on ramp edge model considers all the edges as strong edges,which results in weak edges' distortion.Aiming at this problem,a New Image Interpolation method Based on Ramp EDge model(NIIBRED) is proposed,which uses different methods for strong edges and weak edges.This method considers that the edges generated in the enlarged image do not have the same width,and reconstructs the enlarged image.Experimental results show that NIIBRED can make the enlarged image's edges more natural and clearer,and obtain better texture effects.国家自然科学基金资助项目(60472081);航空科学基金资助项目(05F07001

Dual-tree complex wavelet image denoising based on parental and neighboring coefficients

考虑二维双树复数小波变换(dTCWT)具有良好的平移不变性和方向选择性,基于当前系数与父系数及邻域系数间的关系,构造了dTCWT图像去噪阈值计算公式,提出了一种去噪方法,PndTCWT。该方法在对图像进行二维dTCWT变换后,利用阈值公式,根据当前系数和父系数及相邻系数计算收缩阈值,对当前系数进行去噪处理。最后,经过二维dTCWT反变换,得到去噪结果。实验结果表明,PndTCWT的噪声抑制效果明显优于各种基于dWT的去噪方法和其他dTCWT去噪方法。与基于父系数的dTCWT去噪方法相比,PndTCW的峰值信噪比(PSnr)平均提高了0.5 db左右。从视觉效果来看,PndTCW能在去噪的同时较好地保留图像细节,物体轮廓显得比较平滑,不存在传统dWT算法中的混淆现象。By considering the advantages of the 2D Dual Tree Complex Wavelet Transfer(DTCWT) in shift invariance and directionality,a threshold denoising formula based on parental and neighboring coefficients is constituted and a novel Parental and Neighboring DTCWT(PNDTCWT) image denoising method is presented.By proposed method,the shrinkage threshold of each coefficient is calculated to use in denoising for the current coefficient.After 2D DTCWT transfer to an original image, the final image is obtained by the inverse DTCWT of these denoised coefficients.Experimental results show that the denoising performance of the PNDTCWT is better than those of other denoising methods based on DWT or other DTCWT methods,and its Peak Signal Noise Ratios(PSNRs) have improves by 0.5 dB averagely as compared with that of parental coefficients based DTCWT denoising method.In terms of visual quality,PNDTCWT can get the images with more details,smooth profiles and without confusion effect.航空科学基金资助项目(No.05F07001);国家自然科学基金资助项目(No.60472081

Image Fusion Algorithm Based on Features Motivated Multi-channel Pulse Coupled Neural Networks

Pulse coupled neural networks (PCNN) is a mammal visual cortex-inspired artificial neural networks. Owing to the coupling links in neurons, PCNN is successful to utilize the local information, thus it has been successfully employed in image fusion. However, in traditional PCNN for image fusion, value of per pixel is used to motivate per neuron. In this paper, image feature of per pixel, e.g. gradient and local energy, is used to motivate per neuron and generate firing maps. Each firing map is corresponding to one type feature. Furthermore, a new multi-channel PCNN is presented to combine these firing maps via a weighting function which measures the contribution of these features to the fused image quality. Finally, pixels with maximum firing times, when firing times of source images are compared, are selected as the pixels of the fused image. Experimental results demonstrate that the proposed algorithm outperforms Wavelet-based and Wavelet-PCNN-based fusion algorithms.supported by Navigation Science Foundation of China under grant no. 05F07001 and National Natural Science Foundation of China under grant no. 6047208