基于稀疏正则化的时间图像去噪方法

IF 0.8 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Xin Wang, Xiaogang Dong
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引用次数: 0

摘要

在图像数据传输和采集过程中,经常会出现纹理边缘模糊的现象。为了保证拖拽时间图像的细节清晰度,提出了一种基于稀疏正则化的时间图像去噪方法。首先设置图像像素稀疏度指数,然后基于稀疏正则化处理建立图像去噪模型,得到相似图像块的邻域权值。其次,设计了一种判断编码系数是否达到标准值的时间图像去噪算法,获得了一种新的图像去噪方法;最后,以电子钟和机械钟的图像作为两种时间图像,分别比较了不同的图像去噪方法。结果表明,对于不同噪声标准差和两种时间图像,稀疏化正则化方法的峰值信噪比最高。图像结构相似度始终在以上,表明该方法优于其他五种图像去噪方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time Image De-Noising Method Based on Sparse Regularization
The blurring of texture edges often occurs during image data transmission and acquisition. To ensure the detailed clarity of the drag-time images, we propose a time image de-noising method based on sparse regularization. First, the image pixel sparsity index is set, and then an image de-noising model is established based on sparse regularization processing to obtain the neighborhood weights of similar image blocks. Second, a time image de-noising algorithm is designed to determine whether the coding coefficient reaches the standard value, and a new image de-noising method is obtained. Finally, the images of electronic clocks and mechanical clocks are used as two kinds of time images to compare different image de-noising methods, respectively. The results show that the sparsity regularization method has the highest peak signal-to-noise ratio among the six compared methods for different noise standard deviations and two time images. The image structure similarity is always above which shows that the proposed method is better than the other five image de-noising methods.
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来源期刊
International Journal of Image and Graphics
International Journal of Image and Graphics COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
18.80%
发文量
67
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