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

Denoising Method of Remote Sensing Image Based on Hidden Markov Tree Model in Shearwave Domain

A wave domain hidden Markov tree and remote sensing image technology, applied in the field of image processing, can solve the problems of limited decomposition direction, wavelet transform can not effectively sparse representation, can not fully consider the geometric information of the image and so on.

Inactive Publication Date: 2019-01-11
LIAONING NORMAL UNIVERSITY
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, because the decomposition direction is very limited, the wavelet transform cannot effectively sparsely represent the singularity of lines and planes in the image, and cannot fully consider the geometric information of the image itself in high-dimensional situations, and is prone to block effects

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
  • Denoising Method of Remote Sensing Image Based on Hidden Markov Tree Model in Shearwave Domain
  • Denoising Method of Remote Sensing Image Based on Hidden Markov Tree Model in Shearwave Domain
  • Denoising Method of Remote Sensing Image Based on Hidden Markov Tree Model in Shearwave Domain

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] A method for denoising remote sensing images based on a shear wave domain hidden Markov tree model, characterized in that the steps are as follows:

[0046] Step 1. Input a noisy image;

[0047] Step 2. Perform orientation-adaptive non-subsampled shearlet (NSST) decomposition on the input image:

[0048] Step 2.1 Use the Sobel operator to calculate the pixel gradient of the input image, and then establish a B Gradient orientation histogram of intervals, where B equal to set , make the histogram obtain the minimum number of entropy maximum value;

[0049] Step 2.2 Perform non-subsampled shearlet (NSST) decomposition on the input image, where the number of scales is represent the coarsest scale (low resolution) and the finest scale (high resolution), respectively, and the scale J The number of direction decompositions below is ,in ,and equal to set The element that makes the transformation coefficient entropy of the corresponding subband obtain a larger val...

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 present invention provides a remote sensing image denoising method based on a hidden Markov tree model in the shear wave domain, which belongs to the field of image processing. The present invention uses non-subsampling shear wave transformation to decompose the image in multiple directions and at multiple scales to obtain the image. sparse representation, and uses a hidden Markov tree model to model the distribution of transformation coefficients of the image and its noise, which not only overcomes the frequency aliasing problem of common frequency domain denoising algorithms, but also can denoise easily while Well protect the complex and delicate texture information in the image.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a remote sensing image denoising method based on a shear wave (NSST) domain hidden Markov tree model that can increase the signal-to-noise ratio and improve its visual effect. Background technique [0002] Traditional remote sensing image denoising methods in the transform domain often first perform multi-scale transformation on the image, and then use different methods and models to process the transformed coefficients. Although these methods can effectively capture the detailed information of the image, these algorithms are designed according to the characteristics of the transformed coefficients themselves, but do not fully consider the correlation of multi-scale transformation coefficients within the same scale and between different scales. Using Hidden Markov Tree (HMT) model to model image wavelet coefficients can truly reflect the correlation and dependence of wavelet coeff...

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 Patents(China)
IPC IPC(8): G06T5/00
CPCG06T5/70
Inventor 王相海宋传鸣苏欣朱毅欢
Owner LIAONING NORMAL 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