Performance Analysis & Implementation of Adaptive GMM for Image Restoration & Segmentation

Shilpa Hatwar, A. Wanare
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Abstract

Now days we can easily captured the images and video with the advanced technologies in camera. But this images and video are get easily contaminated by noise due to the characteristics of image sensors due to this they are mostly blurred so we can loss important data. To avoid this problem we proposed an algorithm for segmentation based on Gaussian mixture model (GMM) and restoration technique with spatial smoothness constraints and transform domain techniques. The researchers worked on single type of image but the different environmental images have any noises so that it is not suitable for all images. The proposed algorithm is works on the all diverse field of images which can remove noises from images so it is compatible to all environmental conditions with calculating different image parameter. From all of this we can get the optimum solution of suitable filter for combination of image and noise for reduction of noise by comparing of all of that. Here we also present the algorithm for video segmentation & restoration.
性能分析&;自适应GMM在图像恢复中的实现分割
现在,我们可以很容易地捕捉图像和视频与先进的相机技术。但是由于图像传感器的特性,这些图像和视频容易受到噪声的污染,因此它们大多是模糊的,因此我们可能会丢失重要的数据。为了避免这一问题,提出了一种基于高斯混合模型(GMM)的分割算法,并结合空间平滑约束和变换域技术的恢复技术。研究人员研究的是单一类型的图像,但不同的环境图像存在噪声,因此并不适用于所有图像。该算法适用于所有不同领域的图像,可以去除图像中的噪声,因此可以在计算不同图像参数的情况下兼容所有环境条件。通过对这些方法的比较,得出适合图像与噪声结合的滤波器的最优解,从而达到降噪的目的。本文还介绍了视频分割与恢复算法。
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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