改进的小波图像去噪方法与局部自适应小波图像去噪方法的性能评价与比较

J. Parmar, S. Patil
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引用次数: 46

摘要

噪声的去除是图像恢复过程中的一个重要步骤,但图像去噪一直是图像处理领域的研究热点。去噪是在尽可能保留图像边缘和其他细节特征的同时,从损坏的图像中去除噪声。这种噪声是在采集、传输和接收以及存储和检索过程中引入的。本文采用改进的去噪方法和局部自适应小波图像去噪方法来寻找去噪后的图像。采用基于小波域和空间域的改进小波图像去噪方法和基于小波域的局部自适应小波图像去噪方法对噪声图像进行去噪。本文对改进的小波图像去噪方法和局部自适应小波图像去噪方法的性能进行了评价和比较。基于原始图像与去噪图像的峰值信噪比(峰值信噪比)和原始图像与去噪图像的峰值信噪比对这些方法进行了比较。对一幅图像的仿真和实验结果表明,局部自适应小波图像去噪方法的RMSE小于改进的去噪方法,PSNR高于其他方法。因此去噪后的图像具有更好的视觉效果。本文利用MATLAB实现了这两种方法对图像进行去噪。
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Performance evaluation and comparison of modified denoising method and the local adaptive wavelet image denoising method
Removal of noise is an important step in the image restoration process, but denoising of image remains a challenging problem in recent research associate with image processing. Denoising is used to remove the noise from corrupted image, while retaining the edges and other detailed features as much as possible. This noise gets introduced during acquisition, transmission & reception and storage & retrieval processes. In this paper, to find out denoised image the modified denoising method and the local adaptive wavelet image denoising method can be used. The noisy image is denoised by modified denoising method which is based on wavelet domain and spatial domain and the local adaptive wavelet image denoising method which is based on wavelet domain. In this paper, we have evaluated and compared performances of modified denoising method and the local adaptive wavelet image denoising method. These methods are compared with other based on PSNR (Peak signal to noise ratio) between original image and noisy image and PSNR between original image and denoised image. Simulation and experiment results for an image demonstrate that RMSE of the local adaptive wavelet image denoising method is least as compare to modified denoising method and the PSNR of the local adaptive wavelet image denoising method is high than other method. Therefore, the image after denoising has a better visual effect. In this paper, these two methods are implemented by using MATLAB for denoising of image.
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