A New Method of Denoising Crop Image Based on Improved SVD in Wavelet Domain

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.

Signal-to-Noise Ratio Comparison of Several Filters against Phantom Image

“…However, these filters not only smooth the data to diminish noise but also blur the corresponding edges of the image as well [ 16 ]. A multilayered wavelet transform-based methodology was investigated by [ 17 ] for denoising under the presence of crop images. In their wavelet-based algorithm, they ignored low-frequency coefficients, while expanded high-frequency coefficients on, respectively, horizontal, vertical, and diagonal directions.…”

A Hybrid Image Denoising Method Based on Discrete Wavelet Transformation with Pre-Gaussian Filtering

“…Now the problem is to denoise the approximation part of images with a minimal level of features. The decomposition of the approximation part in the wavelet domain can be represented by equation (7).…”

“…Finally, the inverse of the wavelet transform is performed for image reconstruction. To improve the limitations of linear filters, an improved SVD filter in the transformation domain is proposed (7) . The proposed method gives effective results but the computational complexity is relatively large than other methods.…”

Retracted: A New Method of Denoising Crop Image Based on Improved SVD in Wavelet Domain

“…Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”