In real life, images are inevitably interfered by various noises during acquisition and transmission, resulting in a significant reduction in image quality. The process of solving this kind of problem is called image denoising. Image denoising is a basic problem in the field of computer vision and image processing, which is essential for subsequent image processing and applications. It can ensure that people can obtain more effective information of images more accurately. This paper mainly studies a new method of crop image denoising with improved SVD in wavelet domain. The algorithm used in this study firstly carried out a 3-layer wavelet transform on the crop noise image, leaving the low-frequency subimage unchanged; then, for the high-frequency subimages distributed in the horizontal, vertical, and diagonal directions, the improved adaptive SVD algorithm was used to filter the noise; finally perform wavelet coefficient reconstruction. To effectively test the performance of the algorithm, field crop images were taken as test images, and the denoising performance of the algorithm, SVD algorithm, and the improved SVD algorithm used in this study were compared, and the peak signal-to--to-noise ratio (PSNR) was introduced. Quantitative evaluation of the denoising results of several types of algorithms. The experimental data in this paper show that when the noise standard deviation is greater than 20, the enhanced experimental results clearly achieve higher PSNR and SSIM values than WNNM. The average peak signal-to-noise ratio (PSNR) is about 0.1 dB higher, and the average SSIM is larger about 0.01. The results show that the algorithm used in this study is superior to the other two algorithms, which provides a more effective method for crop noise image processing.

中文翻译：

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一种基于小波域改进SVD的作物图像去噪新方法

在现实生活中，图像在采集和传输过程中不可避免地会受到各种噪声的干扰，导致图像质量显着下降。解决这类问题的过程称为图像去噪。图像去噪是计算机视觉和图像处理领域的一个基本问题，对后续的图像处理和应用至关重要。它可以保证人们更准确地获取更有效的图像信息。本文主要研究了一种小波域改进SVD的裁剪图像去噪新方法。本研究采用的算法首先对作物噪声图像进行3层小波变换，保持低频子图像不变；然后，对于分布在水平、垂直和对角线方向的高频子图像，采用改进的自适应SVD算法滤除噪声；最后进行小波系数重构。为有效测试算法的性能，以大田作物图像作为测试图像，比较了本文所用算法、SVD算法和改进的SVD算法的去噪性能，峰值信号对-- - 引入了噪声比（PSNR）。几种算法去噪结果的定量评估。本文的实验数据表明，当噪声标准偏差大于20时，增强实验结果明显达到了比WNNM更高的PSNR和SSIM值。平均峰值信噪比 (PSNR) 高约 0.1 dB，平均 SSIM 大约 0.01。