Remote sensing image de-noising method based on shearing-wave-domain hidden markov tree model

A wave-domain hidden Markov tree and remote sensing image technology, applied in the field of image processing, can solve problems such as limited decomposition directions, wavelet transform that cannot be effectively sparsely represented, and image geometric information that cannot be fully considered

Inactive Publication Date: 2016-08-24
LIAONING NORMAL UNIVERSITY
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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

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  • Remote sensing image de-noising method based on shearing-wave-domain hidden markov tree model

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Embodiment Construction

[0043] 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:

[0044] Step 1. Input a noisy image;

[0045] Step 2. Perform orientation-adaptive non-subsampled shearlet (NSST) decomposition on the input image:

[0046] 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;

[0047] 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...

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Abstract

The invention, which belongs to the image processing field, provides a remote sensing image de-noising method based on a shearing-wave-domain hidden markov tree model. Multi-direction and multi-scale decomposition is carried out on an image by using non-down-sampling shearing wave transform to obtain a sparse expression of the image; and modeling is carried out on transform coefficient distribution rules of the image and the noise by using a hidden markov tree model. Therefore, a common problem of frequency aliasing of the existing frequency domain de-noising algorithm can be solved; and complicated detained texture information in an image can be protected well during the de-noising process.

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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T5/002
Inventor 王相海宋传鸣苏欣朱毅欢
Owner LIAONING NORMAL UNIVERSITY
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