Image denoising using new wavelet thresholding function

Cai

Security and Communication Networks

2021

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.

Section: Image Denoising Algorithm Singular Value Decomposition (Svd) Is a New Type Of Nonlinear Filtering Algorithmmentioning

confidence: 99%

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

Cai

Security and Communication Networks

2021

“…One of the types of threshold rules, it is applied in discrete wavelet transform and takes the following form (Dehda & Melkemi, 2017), ( Zaeni et al,2018).…”

Section: Hard Threshold Rulementioning

confidence: 99%

Use The Coiflets and Daubechies Wavelet Transform To Reduce Data Noise For a Simple Experiment

Jader¹,

Redha²

2022

IQJOSS

In this research, a simple experiment in the field of agriculture was studied, in terms of the effect of out-of-control noise as a result of several reasons, including the effect of environmental conditions on the observations of agricultural experiments, through the use of Discrete Wavelet transformation, specifically (The Coiflets transform of wavelength 1 to 2 and the Daubechies transform of wavelength 2 To 3) based on two levels of transform (J-4) and (J-5), and applying the hard threshold rules, soft and nonnegative, and comparing the wavelet transformation methods using real data for an experiment with a size of 2 6 observations. The application was carried out through a program in the language of MATLAB. The researcher concluded that using the wavelet transform with the Suggested threshold reduced the noise of observations through the comparison criteria.

J. Appl. Math. Comput. Mech.

“…In recent years, wavelets have gained a lot of popularity and have become a standard tool for many disciplines. So, many authors applied wavelets in images processing [4,5], in mathematics [6] and in other areas of physics and engineering. Wavelet bases with collocation methods have been used for solving single, double and triple integrals [7][8][9], such as in [10] and for   to obtain a faster approximation.…”

Section: Introductionmentioning

confidence: 99%

A new approach to numerical integration based on Coifman wavelets

Dehda¹

2019

Self Cite

In this paper, we present a new approach based on Coifman wavelets to find approximate values of definite integrals. This approach overcomes both CAS and Haar wavelets and hybrid functions in terms of absolute errors. The algorithm based on Coifman wavelets can be easily extended to find numerical approximations for double and triple integrals. Illustrative examples implemented using Matlab show the efficiency and effectiveness of this new method.