Adaptive Gaussian Distribution Threshold Spatial Frequency Denoising Image

Benziane Sarah, Benyamina Abou El Hassen
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Abstract

The details of an image with noise can be restored by removing the noise with an appropriate image denoising method. In this work is proposed and tested, an image denoising methods based on the use of an improved generalized adaptive Gaussian distribution threshold in the wavelet domain. Different wavelet transform methods are used in conjunction with an AGGD threshold to experiment with the proposed approach in order to obtain better results for the image denoising process and, consequently, to select the most suitable filter. The wavelet transform working on the frequencies of separate subbands of an image is a powerful method for image analysis. According to this experimental work, the proposed method show better results. The MSE and PSNR values get are used to measure the enhancement of denoised images.
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自适应高斯分布阈值空间频率图像去噪
通过使用适当的图像去噪方法去除噪声,可以恢复带有噪声的图像的细节。本文提出并测试了一种基于小波域改进广义自适应高斯分布阈值的图像去噪方法。不同的小波变换方法结合AGGD阈值对所提出的方法进行实验,以便在图像去噪过程中获得更好的结果,从而选择最合适的滤波器。小波变换对图像各子带的频率进行处理是一种强大的图像分析方法。实验结果表明,该方法具有较好的效果。用得到的MSE和PSNR值来衡量去噪图像的增强程度。
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