One-dimensional image surface blur algorithm based on wavelet transform and bilateral filtering
One-dimensional image surface blur algorithm based on wavelet transform and bilateral filtering
复制标题
DOI：
10.1007/s11042-021-10754-x
复制
发表时间：
2021-06-09
影响因子：
3.6
通讯作者：
Pang, Mingyong
中科院分区：
文献类型：
--
作者：
Liu, Caixia;Pang, Mingyong
图像噪声通常是在图像的采集、传输和存储过程中产生的，而有些噪声来自图像本身，称为内部噪声。噪声降低了图像的视觉效果和质量。因此，去除图像中的噪声非常重要。本文提出了一种基于小波变换和双边滤波的一维表面模糊算法，用于消除图像内部噪声和保持细节。在我们的算法中，我们首先通过合并图像的每一行和每一列中的像素来将二维图像变换为一维信号向量。然后，我们将每个向量分解为两部分：低频和高频分量与离散小波变换。我们进一步执行双边滤波和局部方差为基础的阈值方法对这两个组成部分的平滑和去噪信号，分别。最后，我们评估我们的算法的性能在一组人脸图像。实验结果表明，该算法在图像去噪和细节保持方面优于传统的平滑方法和现有的一些平滑方法，是一种简单、有效、易于实现的方法，适用于图像平滑，提高了图像的视觉效果和质量.
Image noises are usually generated in the processes of collection, transmission, and storage of images, while some noises, named internal noises, come from the image itself. The noises decrease image visual effect and quality. Thus, it is very important to remove the noises from the images. In this paper, we propose a one-dimensional surface blur algorithm based on wavelet transform and bilateral filtering for image internal noise elimination and detail preservation. In our algorithm, we first transform the two-dimensional image into one-dimensional signal vectors by merging the pixels in each row and column of the image. Then, we decompose each of the vectors into two parts: the low-frequency and high-frequency components with a discrete wavelet transform. We further perform the bilateral filtering and a local variance-based thresholding method on the two components to smooth and denoise signals, respectively. Finally, we evaluate our algorithm's performance in a group of face images. The experimental results show that our algorithm achieved better performance on image denoising and detail preservation than a set of traditional smoothing methods and the state-of-the-art. Our algorithm is a simple, effective, and easy-to-implement method, and it is suitable for image smoothing to improve the image's visual effect and quality.