Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Self-adaptive wavelet threshold image de-noising algorithm and device

A wavelet threshold and self-adaptive technology, applied in the field of image denoising, can solve the problems of strangling high-energy signals and affecting the effect of noise reduction

Active Publication Date: 2016-08-31
JINAN UNIVERSITY
View PDF5 Cites 34 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although the global threshold calculation is simple, it will lead to the Gibbs effect at the discontinuity point and overkill the high-energy signal, thus affecting the noise reduction effect

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Self-adaptive wavelet threshold image de-noising algorithm and device
  • Self-adaptive wavelet threshold image de-noising algorithm and device
  • Self-adaptive wavelet threshold image de-noising algorithm and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0072] Such as figure 1 As shown, this embodiment discloses an adaptive wavelet threshold image denoising algorithm, comprising the following steps:

[0073] S1. Perform wavelet decomposition on the image signal: select an appropriate wavelet and set the highest level N of decomposition, and calculate the wavelet coefficients of the image signal s(i, j) at each level;

[0074] S11, such as figure 2 As shown, the noise-containing image is subjected to 3-layer wavelet transform, and after decomposition, low-frequency coefficients and high-frequency coefficients whose directions are horizontal, vertical, and diagonal are generated. The energy of the signal and the important feature information of the image are mainly concentrated in the low-frequency smooth component LL superior.

[0075] S12, according to the wavelet coefficients of each layer obtained in step S11, considering its signal correlation, the coefficients in the adjacent regions of each coefficient are averaged: ...

Embodiment 2

[0109] Such as Figure 4 As shown, this embodiment discloses an adaptive wavelet threshold image denoising device, the device includes the following modules:

[0110] The wavelet decomposition module is used to carry out wavelet decomposition to the image signal, select the appropriate wavelet and set the highest level N of decomposition, and calculate the wavelet coefficients of the noisy image signal s (i, j) in each layer;

[0111] The wavelet coefficient module is used for the threshold value processing of wavelet coefficients. The appropriate threshold value is determined in each layer and direction of wavelet decomposition, and the appropriate threshold value function is used to process the detail wavelet coefficients of each layer, so as to retain the wavelet coefficient of the image signal as much as possible. Principle, make the wavelet coefficient of the noise zero;

[0112] The image reconstruction module is used to reconstruct the image signal, and reconstructs the ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention brings forward a self-adaptive wavelet threshold image de-noising algorithm and device. The image de-noising algorithm comprises the following steps: a noised image is subjected to wavelet transformation operation, and wavelet coefficients of all layers can be obtained; with signal correlation considered, coefficients in an area adjacent to each coefficient are averaged in wavelet coefficients of each layer; threshold is determined based on a wavelet coefficient which is obtained via an absolute mean value estimation method, and a self-adaptive threshold method is adopted for determining thresholds suitable for all different scales; as for the wavelet coefficients and thresholds, self-adaptive threshold functions for all directions at all layers are constructed, wavelet inverse transformation and reconstruction are performed, and a de-noised image can be obtained. According to the image de-noising algorithm, the self-adaptive threshold method is adopted for determining the thresholds, an overall uniform threshold is replaced with thresholds for different scales, wavelet threshold de-noising operation is performed via use of the self-adaptive thresholds and the self-adaptive threshold functions, and detailed information of the image can be protected; the self-adaptive wavelet threshold image de-noising algorithm is better than a conventional wavelet threshold de-noising algorithm in terms of peak signal to noise ratio and visual perception.

Description

technical field [0001] The invention relates to the technical field of image denoising in digital image processing, in particular to an adaptive wavelet threshold image denoising algorithm and device. Background technique [0002] During the process of digital image generation, it will be affected by reasons such as sensor vibration, electronic device interference, etc., resulting in a decline in the quality of the converted digital image and affecting the understanding of the image content. In order to ensure the correctness of subsequent processing, it is necessary to denoise the image. The application of image denoising technology has expanded from the aerospace field to various fields and industries such as biomedicine, information science, resource and environmental science, astronomy, physics, industry, agriculture, national defense, education, art, etc. significant impact on everyday life. Therefore, the research on image denoising technology has extremely important...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T5/70
Inventor 石敏王耿易清明
Owner JINAN UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com