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Morphological component analysis (MCA)-based synthetic aperture radar (SAR) image noise suppression method

An image and square matrix technology, applied in image enhancement, image data processing, reflection/re-radiation of radio waves, etc., can solve the problem of not being able to achieve good noise suppression and edge preservation effects at the same time, and achieve the effect of noise suppression

Inactive Publication Date: 2010-12-08
HAIAN TEXTILE MACHINERY +1
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Problems solved by technology

[0006] In order to avoid the deficiencies of the prior art, the present invention proposes an MCA-based SAR image noise suppression method, which overcomes the disadvantages that the prior art methods cannot achieve better noise suppression and edge preservation effects at the same time

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  • Morphological component analysis (MCA)-based synthetic aperture radar (SAR) image noise suppression method
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  • Morphological component analysis (MCA)-based synthetic aperture radar (SAR) image noise suppression method

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Embodiment Construction

[0021] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0022] 1) The input SAR image gray value matrix is ​​mirrored and extended to a square matrix whose side length is 2 to the power of the exponential, denoted as S. If the size of the input image is 300×300, then the size of the expanded image S is 512×512. First, the original image is copied to the center of the template square matrix, and then the surrounding (512-300) / 2=106 pixels are respectively processed. Symmetrical expansion.

[0023] 2) Take the logarithm of S, so that the multiplicative coherent speckle noise in the SAR image is transformed into an approximate Gaussian additive noise, and the logarithmic image S′ is obtained.

[0024] 3) Select the dictionary combination of curvelet and LDCT, and perform curvelet and LDCT transformation on S′ respectively to obtain C C (the transformation coefficient matrix corresponding to curvelet) and C L (The tran...

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Abstract

The invention relates to a morphological component analysis (MCA)-based synthetic aperture radar (SAR) image noise suppression method. The method is technically characterized in that: an MCA method is applied to the suppression of multiplicative noises in an SAR image; and because the image generally comprises the two morphological components of edge / contour and texture, in the method, the dictionary assembly of curvelet and local discrete cosine transform (LDCT) is utilized under the uniform frame of MCA according to the good edge / contour representation characteristics of the curvelet and the high partial texture expression capability of the LDCT to fulfill the aim of suppressing the noises in the SAR image. Therefore, for a noise image, effective components of the image can be separated and the noises are kept in the remnant image by the method to fulfill the aim of suppressing the noises. Experimental results on both simulated and real SAR images show that the method has the advantage of relatively better noise suppression effect compared with the conventional wavelet-based methods and curvelet-based methods.

Description

technical field [0001] The invention relates to an MCA-based SAR image noise suppression method, in particular to a SAR image noise suppression method based on morphological component analysis. Background technique [0002] Due to the imaging method of coherent microwave irradiation, Synthetic Aperture Radar (SAR) images are inevitably affected by speckle noise. The existence of speckle noise hides the details of the image, reduces the gray resolution of the image, and seriously affects the correct recognition and interpretation of the SAR image. Therefore, it is of great significance to suppress the coherent speckle noise in SAR images. [0003] The early SAR image noise suppression methods were mainly based on spatial domain and filtering methods, but these methods damaged the details of the original image while suppressing coherent speckle noise well. Therefore, the wavelet transform is used to overcome these shortcomings, but the latest research shows that due to the i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G01S13/90
Inventor 李映龚红丽张艳宁
Owner HAIAN TEXTILE MACHINERY
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