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Hidden Markov tree model based method for de-noising SAR image

An image and model technology, applied in the field of image processing, can solve the problems of low equivalent visual number, unsatisfactory denoising effect, and insufficient noise removal in homogeneous regions, and achieve the effect of improving the equivalent visual number

Inactive Publication Date: 2010-03-31
XIDIAN UNIV
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Problems solved by technology

However, this model only captures the dependence in a small scale. When it is used for SAR image denoising, some details and texture information are smoothed, and the noise removal in the homogeneous area is not thorough enough, resulting in a low equivalent view number in the homogeneous area. Noise reduction is not ideal

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Embodiment Construction

[0028] refer to figure 1 , the concrete realization steps of the present invention are as follows:

[0029] Step 1: Perform logarithmic transformation on the SAR image, and convert the multiplicative noise into additive white Gaussian noise for processing: logy=logz+logx, where y represents the input SAR image, z represents the noise image, and x represents the image without noise, Two sets of directional filters with different directions are used. The directions of the two sets of directional filters are 4, 4, 4 and 4, 8, and 8 respectively. Contourlet decomposition is performed on the logarithmically transformed data to obtain the Contourlet transform coefficients. respectively y 1 and y 2 .

[0030] Step 2: Transform coefficients y 1 Establish a unidirectional transfer HMT model, such as figure 2 As shown in (a), the black square is the parent node, the four empty squares are its child nodes, and the parameter set of the model is Θ 1 ; for the transform coefficient ...

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Abstract

The invention discloses a hidden Markov tree model based method for de-noising an SAR image, which relates to the field of image processing, and mainly solves the problems that details and texture information of an image are smoothed and an equivalent noise level in a homogeneous region is low. The method comprises the following steps: (1) carrying out logarithmic transformation and Contourlet decomposition; (2) carrying out HMT modeling on Contourlet coefficients, and training the model; (3) correcting the Contourlet coefficients by using estimated parameters; (4) establishing a background hidden Markov training model, correcting the coefficients again by using the estimated parameters, and carrying out Contourlet inverse transformation and exponential transformation to acquire a primaryde-noised image; (5) de-noising differential images to obtain a secondary de-noised image; and (6) combining the two de-noised images, and carrying out rotary translation on the combined image to acquire and output a final de-noised image. The method well maintains the details and texture information of the SAR image, reduces speckle noise in the homogeneous region of the SAR image, and can be used for de-noising the SAR image.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for denoising SAR images, which can be used for denoising SAR images, natural images and medical images. Background technique [0002] Synthetic Aperture Radar SAR, as an active radar, is not affected by light and weather conditions. It has been widely used in marine, disaster, mapping and military fields. The existence of speckle noise in SAR images caused by the coherence of the scattering echoes of imaging scatterers is not conducive to the automatic analysis of the scene in the image and the understanding of the SAR image, which makes the interpretation of the image difficult, especially for the SAR images. The effects of point targets and edges are very noticeable. Therefore, the removal of speckle noise is very important for subsequent processing of SAR images such as edge detection. [0003] In recent years, speckle noise suppression technology in SAR ...

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Application Information

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IPC IPC(8): G01S13/90G06T5/00
Inventor 侯彪焦李成田福苓王爽张向荣马文萍
Owner XIDIAN UNIV