Dual-Tree Complex Wavelet Coefficient Magnitude Modeling Using Scale Mixtures of Rayleigh Distribution for Image Denoising

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: A Literature Reviewmentioning

confidence: 99%

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

Cai

Security and Communication Networks

2021

Advances in Parallel Computing

“…DT-CWT is a newly formed transform which offer both near shift invariance and enhanced properties of directional selectivity. [13] considered an effective method of Discrete Wavelet Transform (DWT) for the analysis of EEG time-frequency and were employed for detection of seizure in the daily monitoring systems of healthcare. DTDWT might conquer those drawback however might augment redundancy of information [14].At present, the image denoising depending on sparse representation has a superior outcome [15].…”

Section: Related Workmentioning

confidence: 99%

An Effective Denoising and Enhancement Strategy for Medical Image Using Rl-Gl-Caputo Method

Ganga

Janakiraman

Sivaraman

et al. 2021

At present, fractional differential is the effective mathematical approach which deals with the factual problems. This projected technique employs the fractional derivatives definitions Riemann-Liouville (R-L), Grunwald-Letnikov (G-L) and the caputo technique for denoising medical image. The presented method based on fractional derivative which in turn improves the quality of image. The input image is processed on integer order method such as pre-processing operation, image conversion and noise image. The fractional differential mask method is to be applied with the help of Riemann Liouville, and Caputo algorithm. After denoising the medical image enhanced using Anisotropic diffusion process and the result is analyzed to finally get denoised and predicted image.

“…Data compression, data interpretation, data hiding, audio signal processing, motion tracking, and machine learning. It has been recognized that the DWT suffers from shift variance [1], [2] and high computational competence, and memory necessities. Dual tree complex wavelet transform and lifting wavelet transform (LWT) are developed to beat these drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Lifting dual tree complex wavelets transform

Naimi

Adamou-Mitiche

Mitiche

2021

IJECE

We describe the lifting dual tree complex wavelet transform (LDTCWT), a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). We describe a way to estimate the accuracy of this approximation and style appropriate filters to attain this. These benefits are often exploited among applications like denoising, segmentation, image fusion and compression. The results of applications shrinkage denoising demonstrate objective and subjective enhancements over the dual tree complex wavelet transform (DTCWT). The results of the shrinkage denoising example application indicate empirical and subjective enhancements over the DTCWT. The new transform with the DTCWT provide a trade-off between denoising computational competence of performance, and memory necessities. We tend to use the PSNR (peak signal to noise ratio) alongside the structural similarity index measure (SSIM) and the SSIM map to estimate denoised image quality.