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A method of applying lifting-based wavelet domain Wiener filter (LBWDMF) in image enhancement is proposed. Lifting schemes have emerged as a powerful method for implementing biorthogonal wavelet filters. They exploit the similarity of the filter coefficients between the low-pass and high-pass filters to provide a higher speed of execution, compared to classical wavelet transforms. LBWDMF not only helps in reducing the number of computations but also achieves lossy to lossless performance with finite precision. The proposed method utilises the multi-scale characteristics of the wavelet transform and the local statistics of each subband. The proposed method transforms an image into the wavelet domain using lifting-based wavelet filters and then applies a Wiener filter in the wavelet domain and finally transforms the result into the spatial domain. When the peak signal-to-noise ratio (PSNR) is low, transforming an image to the lifting-based wavelet domain and applying the Wiener filter in the wavelet domain produces better results than directly applying Wiener filter in spatial domain. In other words each subband is processed independently in the wavelet domain by a Wiener filter. Moreover, in order to validate the effectiveness of the proposed method the result obtained using the proposed method is compared to those using the spatial domain Wiener filter (SDWF) and classical wavelet domain Wiener filter (CWDWF). Experimental results show that the proposed method has better performance over SDWF and CWDWF both visually and in terms of PSNR. 相似文献
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为了有效抑制红外图像中的随机噪声,采用一种基于提升小波变换的双重滤波算法来进行处理。该算法对含有噪声的红外图像实现第1次提升小波分解,然后对获得的低频和高频分解系数再次实现提升小波变换,舍弃由低频系数经过第2次提升小波变换后获得的低频系数以及由高频系数经过第2次提升小波变换后获得的高频系数。对剩余的高频系数和低频系数分别采用改进阈值函数模型以及改进非局部均值滤波算法进行处理,在此基础上实现小波系数重构。为了改善滤波后图像视觉效果,再引入直方图均衡化算法进行处理。通过理论分析和实验验证,获得了相关的标准测试图像和红外图像测试结果以及峰值信噪比和结构相似度测试数据。结果表明,该滤算法对于高质量地去除红外图像中的噪声是有帮助的。 相似文献
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一种基于小波变换的自适应图像降噪法 总被引:2,自引:0,他引:2
提出了一种基于二进小波变换的图像降噪方法,通过对小波变换系数进行阈值处理实现降噪。该方法结合图像的自身邻域信息,具有一定的自适应性。实验结果证明能够产生较好的效果。 相似文献
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Combining spatial and scale-space techniques for edge detection to provide a spatially adaptive wavelet-based noise filtering algorithm 总被引:4,自引:0,他引:4
New methods for detecting edges in an image using spatial and scale-space domains are proposed. A priori knowledge about geometrical characteristics of edges is used to assign a probability factor to the chance of any pixel being on an edge. An improved double thresholding technique is introduced for spatial domain filtering. Probabilities that pixels belong to a given edge are assigned based on pixel similarity across gradient amplitudes, gradient phases and edge connectivity. The scale-space approach uses dynamic range compression to allow wavelet correlation over a wider range of scales. A probabilistic formulation is used to combine the results obtained from filtering in each domain to provide a final edge probability image which has the advantages of both spatial and scale-space domain methods. Decomposing this edge probability image with the same wavelet as the original image permits the generation of adaptive filters that can recognize the characteristics of the edges in all wavelet detail and approximation images regardless of scale. These matched filters permit significant reduction in image noise without contributing to edge distortion. The spatially adaptive wavelet noise-filtering algorithm is qualitatively and quantitatively compared to a frequency domain and two wavelet based noise suppression algorithms using both natural and computer generated noisy images. 相似文献
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提升小波具有结构简单、运算量低、原位运算、节省缓存空间,逆变换通过结构翻转得到等诸多易于实现的特点。将基于Deslaufiers-Dubuc(4,2)小波的提升方案应用于图像的去躁处理中,能快速有效去除信号中的高斯白噪声等。将提升方案与中值滤波相结合,可同时滤除图像中的高斯白噪声和脉冲噪声等。 相似文献
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利用小波阈值去噪方法和传统空间域Lee 滤波的特点, 提出了一种图像去噪的的组合滤波方案。首先在小波域对图像阈值去噪, 得到预去噪图像; 再在空间域上利用自适应Wiener 滤波器进一步提高恢复图像的精度。为了保证小波域和空间域两种算法之间的匹配, 对预去噪图像中残留噪声的分布进行了研究, 对其噪声方差估计做了改进, 提出了一种估计噪声方差的近似最优公式。仿真实验表明, 与单独的在小波域或空域去噪相比, 该方法的均方误差和信噪比指标均得到了改善。 相似文献
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Image fusion is a technical method to integrate the spatial details of the high‐resolution panchromatic (HRP) image and the spectral information of low‐resolution multispectral (LRM) images to produce high‐resolution multispectral images. The most important point in image fusion is enhancing the spatial details of the HRP image and simultaneously maintaining the spectral information of the LRM images. This implies that the physical characteristics of a satellite sensor should be considered in the fusion process. Also, to fuse massive satellite images, the fusion method should have low computation costs. In this paper, we propose a fast and efficient satellite image fusion method. The proposed method uses the spectral response functions of a satellite sensor; thus, it rationally reflects the physical characteristics of the satellite sensor to the fused image. As a result, the proposed method provides high‐quality fused images in terms of spectral and spatial evaluations. The experimental results of IKONOS images indicate that the proposed method outperforms the intensity‐hue‐saturation and wavelet‐based methods. 相似文献
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在针对传统的多尺度分解的融合方法运算速度慢、内存需求量大,不适于实时应用的局限性的基础上,提出了一种基于提升小波变换的图像融合算法。多个源图像分别进行提升小波分解,使用恰当的融合规则合并各尺度对应的分解系数,通过提升小波逆变换得到融合图像。实验结果表明,提出的算法无论在执行时间还是融合图像质量上都优于传统方法,有广泛的应用前景,特别适用于实时系统。 相似文献
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基于具有对称性的非张量积小波图像融合方法 总被引:1,自引:0,他引:1
提出了基于一类新的小波具有紧支撑、对称性和正交性、伸缩矩阵为(20 02)的非张量积小波的图像融合新方法.首先根据非张量积小波理论,提出了一种新的二维4通道4× 4对称滤波器组的设计方法,并用此方法设计出一组具有上述性质的非张量积小波4× 4滤波器组,利用此滤波器组对参加融合的图像进行滤波;然后对低频部分采用取均值、高频部分采用基于局部窗口能量取大的融合算法对滤波后的图像进行融合;最后重构.并采用熵、交叉熵、互信息、均方根误差和峰值信噪比等指标对该方法的融合性能进行了客观评价.对可见光图像与红外图像、远红外图像与近红外图像、航空图像和卫星图像、多聚焦图像等其它多类图像的融合实验结果表明本方法有较好的融合效果,可得到无边缘失真的融合结果图像,其融合性能比采用同样融合算法的张量积Haar小波的融合方法的融合性能好. 相似文献
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面对采集的指纹图像质量较差的问题,提出一种在小波域对指纹图像滤波增强算法。该算法在小波域进行Ga-bor滤波增强,通过Radon变换后的投影估计Gabor滤波的方向和频率,对滤波增强后的子图进行小波重构得到最后的结果,有效地抑制高频扰动对指纹图像质量的影响,提高运算速度和准确性。通过对FVC2000指纹库中的部分低质量指纹图像进行增强,表明该算法对指纹图像的增强效果明显,并且处理速度较快。 相似文献
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基于噪声分离和小波阈值自适应图像去噪算法 总被引:1,自引:0,他引:1
针对VisuShrink小波阈值滤波算法的不足和混合噪声的情况,提出了一种基于噪声分离和尺度的自适应混合图像去噪算法.算法首先通过极值检测分离脉冲噪声和高斯噪声,然后分别对脉冲噪声应用多窗口中值滤波及高斯噪声应用基于尺度的小波阈值滤波完成去噪.实验表明,该混合滤波算法能有效去除图像中的脉冲噪声和高斯噪声,并较好地保存了... 相似文献
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提出了用计算全息制作三类Haar子波滤波器的方法。将一维Haar子波函数组合,将得到三种类型的二维Haar子波函数,利用这三类二维Haar子波可以分别对图像进行角、水平边和垂直边的提取。采用罗曼Ⅲ型迂回位相编码法对三类Haar子波函数进行编码,绘出计算全息图。为了提高全息图的质量,采用显示器分屏抓图的办法,将全息图分割输出。在4f系统中实现对二维图像的三类Haar子波变换,给出了提取二值图像特征的模拟结果。 相似文献
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B. K. Shreyamsha Kumar 《Signal, Image and Video Processing》2013,7(6):1211-1227
Non-local means filter uses all the possible self-predictions and self-similarities the image can provide to determine the pixel weights for filtering the noisy image, with the assumption that the image contains an extensive amount of self-similarity. As the pixels are highly correlated and the noise is typically independently and identically distributed, averaging of these pixels results in noise suppression thereby yielding a pixel that is similar to its original value. The non-local means filter removes the noise and cleans the edges without losing too many fine structure and details. But as the noise increases, the performance of non-local means filter deteriorates and the denoised image suffers from blurring and loss of image details. This is because the similar local patches used to find the pixel weights contains noisy pixels. In this paper, the blend of non-local means filter and its method noise thresholding using wavelets is proposed for better image denoising. The performance of the proposed method is compared with wavelet thresholding, bilateral filter, non-local means filter and multi-resolution bilateral filter. It is found that performance of proposed method is superior to wavelet thresholding, bilateral filter and non-local means filter and superior/akin to multi-resolution bilateral filter in terms of method noise, visual quality, PSNR and Image Quality Index. 相似文献
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结构光光条图像通常受到大量噪声的干扰,会对光条图像分析的造成影响。本文通过对结构光光条图像的噪声特点的分析,结合中值滤波和小波去噪特性,提出基于自适应中值滤波和改进小波重构的去噪方法。用本文提出的方法对结构光光条图像进行去噪处理,并与传统小波软阈值去噪法等其他去噪方法结果进行对比。使用客观的评价标准对两种方法去噪效果进行评价,结果表明,本文提出的去噪方法对结构光光条图像有更好的去噪效果。 相似文献