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Double Density Wavelet with Fast Bilateral Filter based Image Denoising for WMSN

机译:基于快速双边滤波器的WMSN双密度小波图像去噪

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Removal of Gaussian noise from the sensor imageduring acquisition is such a difficult task in the field of Wireless Multimedia Sensor Network (WMSN). There are several image denoising algorithms proposed so far in the field of WMSN to reduce the impact of noise over images. Out of various denoising algorithms, wavelets have shown superior performance as image denoising technique. However, the properties of wavelets may affect the denoised output significantly for different set of images. In order to avoid this, an attempt has been made in this paper to reduce the Gaussian noise during image acquisition by utilizing the double density dual tree wavelet transform. Further, the quality of the image can be enhanced by appending the Fast Bilateral Filter (FBF) with the double density wavelets. To understand the behavior of the double density wavelet based image denoising technique, it is simulated and compared with the existing dual tree complex wavelet transform based denoising technique for different noise levels by using MATLAB simulation
机译:在无线多媒体传感器网络(WMSN)领域,从传感器成像过程中去除高斯噪声是一项艰巨的任务。迄今为止,在WMSN领域中提出了几种图像去噪算法,以减少噪声对图像的影响。在各种降噪算法中,小波已显示出作为图像降噪技术的优越性能。但是,对于不同的图像集,小波的属性可能会显着影响去噪输出。为了避免这种情况,本文尝试通过利用双密度双树小波变换来减少图像获取期间的高斯噪声。此外,可以通过向快速双边滤波器(FBF)附加双密度小波来提高图像质量。为了了解基于双密度小波的图像去噪技术的行为,通过使用MATLAB仿真对它进行了仿真,并与现有的针对不同噪声水平的基于双树复小波变换的去噪技术进行了比较。

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