基于图像质量评价和非局部均值图像去噪方法的研究
发布时间：2018-10-31 13:44
【摘要】：作为图像预处理手段之一,图像去噪在图像处理领域发挥着重要作用。基于非局部均值的图像去噪算法是图像去噪研究的重点。图像质量评价是对图像视觉质量进行评价的方法。本文研究了基于非局部均值和图像质量评价的图像去噪算法。在基于图像自相似性进行图像去噪的情况下,一般是利用欧式距离进行图像块的相似性度量。一些客观的图像质量评价方法不仅考虑图像的像素,还考虑图像的亮度、对比度、结构等信息。与只用欧式距离考虑图像块像素值的差异相比,本文研究了把客观图像质量评价方法与欧式距离结合起来寻找图像相似块的方法,寻找到的相似块会在结构等图像信息方面有更多相似。自然状况下的图像通常包含多种噪声,并且不容易分辨。通常是用高斯分布模型对图像分布模型进行模拟进而寻找相似块,但是高斯分布模型不一定适用于多种噪声混合下的图像,因而需要考虑图像分布模型的自适应情况。本文研究了自适应软阈值为基础的图像去噪方法,经过实验对比,本文提出的方法取得了较好的去噪效果。
[Abstract]:As one of the means of image preprocessing, image denoising plays an important role in the field of image processing. Image denoising algorithm based on non-local mean is the focus of image denoising. Image quality evaluation is a method to evaluate image visual quality. An image denoising algorithm based on non-local mean and image quality evaluation is studied in this paper. In the case of image denoising based on image self-similarity, Euclidean distance is generally used to measure the similarity of image blocks. Some objective image quality evaluation methods not only consider the pixels of the image, but also consider the brightness, contrast and structure of the image. Compared with only using Euclidean distance to consider the pixel value of image block, this paper studies the method of combining objective image quality evaluation method with Euclidean distance to find image similar block. The similar blocks will be more similar in structure and other image information. Natural images usually contain multiple noises and are not easily discernible. Gao Si distribution model is usually used to simulate the image distribution model and to find similar blocks. However, Gao Si distribution model is not necessarily suitable for images with multiple noises, so it is necessary to consider the adaptive image distribution model. The image denoising method based on adaptive soft threshold is studied in this paper.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41
本文编号：2302412
[Abstract]:As one of the means of image preprocessing, image denoising plays an important role in the field of image processing. Image denoising algorithm based on non-local mean is the focus of image denoising. Image quality evaluation is a method to evaluate image visual quality. An image denoising algorithm based on non-local mean and image quality evaluation is studied in this paper. In the case of image denoising based on image self-similarity, Euclidean distance is generally used to measure the similarity of image blocks. Some objective image quality evaluation methods not only consider the pixels of the image, but also consider the brightness, contrast and structure of the image. Compared with only using Euclidean distance to consider the pixel value of image block, this paper studies the method of combining objective image quality evaluation method with Euclidean distance to find image similar block. The similar blocks will be more similar in structure and other image information. Natural images usually contain multiple noises and are not easily discernible. Gao Si distribution model is usually used to simulate the image distribution model and to find similar blocks. However, Gao Si distribution model is not necessarily suitable for images with multiple noises, so it is necessary to consider the adaptive image distribution model. The image denoising method based on adaptive soft threshold is studied in this paper.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41
【参考文献】
相关期刊论文 前1条
1 路文;高新波;王体胜;;一种基于WBCT的自然图像质量评价方法[J];电子学报;2008年02期
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