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A Gaussian proportional mixture model for sparse representation sar image de-speckling method

A sparse representation and proportional mixing technology, applied in the field of image filtering research, which can solve the problems of inability to suppress coherent speckle noise as much as possible, the effect of speckle reduction is not as good as PPB, and the loss of image detail information.

Active Publication Date: 2020-05-29
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

However, although the PPB algorithm can suppress the coherent speckle better, it will introduce a large number of artificial artifacts, resulting in a large loss of image detail information. The SAR-BM3D and FANS algorithms are relatively better in maintaining detail information, but the speckle reduction effect is not as good as PPB. , unable to suppress coherent speckle noise as much as possible

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  • A Gaussian proportional mixture model for sparse representation sar image de-speckling method
  • A Gaussian proportional mixture model for sparse representation sar image de-speckling method
  • A Gaussian proportional mixture model for sparse representation sar image de-speckling method

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Embodiment

[0099] A method for reducing speckle in a sparsely represented SAR image based on the GSM model in this embodiment, first uses a method of probability estimation to divide the SAR image into several sets of similar blocks subject to the same probability and statistical characteristics, and according to the principle of sparse representation The image block is modeled to obtain a convex optimization mathematical model. Then the Bayesian estimation and the probability density function of the coherent speckle are combined to perform GSM modeling on the sparse coefficients in the model, and a sparse representation model based on GSM is obtained. Since similar image blocks in each set obey the same mathematical statistics, they satisfy the same sparse representation. Using the above modeling method to establish a mathematical model for a set of several image blocks, the solution of the model uses iterative regularization, and after the optimal solution of the convex optimization mo...

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Abstract

The invention relates to a sparse representation SAR image despeckling method based on a Gaussian scale mixture model, and belongs to the field of image filtering research. According to the method, firstly a sparse representation model of a single image block is established; then the sparse coefficient alpha is represented by the GSM model according to the statistical property of coherent speckles and the Bayesian estimation principle so as to obtain an optimized model; meanwhile, SAR images are classified and a sparse model is established according to the classification result; and finally the model is solved by using a convex optimization method so that the optimal sparse representation can be obtained and thus the denoised images can be obtained. According to the method, the coherent speckles can be effectively suppressed and the detailed information of the SAR images can be greatly reserved so as to achieve the great image reconstruction quality.

Description

technical field [0001] The invention relates to a sparse representation SAR image speckle reduction method based on a Gaussian proportional mixture model, which belongs to the field of image filtering research. Background technique [0002] Synthetic Aperture Radar (SAR) has become a representative radar system in the field of microwave remote sensing due to its high-resolution imaging capabilities in azimuth and range, and has attracted widespread attention in both military and civilian applications. When the radar emits electromagnetic signals to irradiate the target, the interference between the random scattering signal of the target and the transmitted signal will produce coherent speckle noise, which seriously affects the image quality, so the suppression of coherent speckle noise is particularly important. The most direct way to eliminate speckle noise is to use multi-view processing, but this method will reduce the azimuth resolution. A more reasonable way is to use f...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
CPCG06T5/002G06T2207/10044
Inventor 何碧容蔡倩孔莹莹周建江
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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