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Efficiency of wavelet coefficients thresholding techniques used for multimedia and astronomical image denoising

机译:用于多媒体和天文图像去噪的小波系数阈值技术的小波系数阈值技术

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This paper deals with image denoising based on the wavelet transform realized by Mallat algorithm and À trous algorithm. The effectiveness of global and subband thresholding techniques are studied on multimedia and astronomical images contaminated by Gaussian noise. Experimental results on several testing images are compared with each other from two objective quality aspects (PSNR, RMSE). Astronomical image denoising techniques differ from those used for multimedia images, because astronomical data are processed by computers and are not evaluated by humans. Thus we show especially the difference between quality criteria related with both types of images after denoising. In case of astronomical data, important scientific criteria as stellar magnitude and FWHM (Full Width at Half Maximum) changes are studied in processed images after noise removal.
机译:本文涉及基于由Mallat算法和#00C0实现的小波变换的图像去噪; 真实的算法。 通过高斯噪声污染的多媒体和天文图像研究了全局和子带阈值阈值技术的有效性。 从两个客观质量方面(PSNR,RMSE)相互比较了几个测试图像上的实验结果。 天文图像去噪技术与用于多媒体图像的技术不同,因为天文数据由计算机处理,并且不被人类评估。 因此,我们特别展示了在去噪后与两种类型图像相关的质量标准之间的差异。 在天文数据的情况下,在噪声移除后,在处理的图像中研究了作为恒星幅度和FWHM(半最大宽度)变化的重要科学标准。

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