Abstract
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.
Highlights
IntroductionErefore, using appropriate methods to remove or reduce these noises is an essential preprocessing step, and it is one of the most basic and important tasks in the field of agricultural informatization and automation research
In view of the large amount of random noise that often appears in crop images, on the basis of in-depth analysis of the basic principles of the Singular value decomposition (SVD) denoising algorithm, combined with wavelet transform, an improved adaptive SVD denoising algorithm in the wavelet domain is proposed
E performance of the algorithm is tested through crop images, and the performance is qualitatively and quantitatively compared with the SVD algorithm and the improved SVD algorithm. e results show that the performance of the algorithm used in this research is slightly better than the other two algorithms, which provides for the processing of crop noise images
Summary
Erefore, using appropriate methods to remove or reduce these noises is an essential preprocessing step, and it is one of the most basic and important tasks in the field of agricultural informatization and automation research. E key problem in image processing is to identify the information required for interference and recovery. According to the image characteristics and Security and Communication Networks interference distribution, researchers have proposed various forms of denoising methods. E final purpose of denoising is to solve the problem of image quality degradation caused by noise interference. It is an important preprocessing method that lays a good foundation for the subsequent digital image processing. E noise in the image is often interwoven with the signal It is an important preprocessing method that lays a good foundation for the subsequent digital image processing. e noise in the image is often interwoven with the signal
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