Noisy image restoration based on optimized cellular neural network

Nima Aberomand, S. M. Jameii
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引用次数: 1

Abstract

Sending multimedia file in computer networks from the source to destination can cause noisy. Some images have default noises. Other types of images in processing can be noisy due to high level process in weak systems. Creating a system for image retrieval is an important part of image processing. This article focused on noisy image restoration based on cellular neural network. Noises inside the pixel with different sizes are restored with different levels of surrounding information. Images with 50% of noise cannot be recovered correctly, but optimized cellular neural network can recover whole part of images with less noises. The main purposes of using cellular neural network are less time with more noise removal. Two evaluation methods like MSE and PSNR are used to compare with recent methods.
基于优化细胞神经网络的噪声图像复原
在计算机网络中,多媒体文件从源端传输到目的端会产生噪声。有些图像有默认的噪音。在弱系统中,由于高阶处理,处理中的其他类型的图像可能会有噪声。建立图像检索系统是图像处理的重要组成部分。本文主要研究了基于细胞神经网络的噪声图像复原方法。采用不同大小的像素内部噪声,利用不同层次的周围信息进行恢复。对于含有50%噪声的图像不能正确恢复,而优化后的细胞神经网络可以恢复含有较少噪声的图像的全部部分。使用细胞神经网络的主要目的是更少的时间和更多的噪声去除。采用MSE和PSNR两种评价方法与现有方法进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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