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SAR image despeckling method based on texture enhancement and sparse coding

A sparse coding and image technology, applied in the field of image processing, can solve problems such as block effect and over-smoothing, and achieve the effect of maintaining radiation characteristics and enhancing speckle reduction effect.

Active Publication Date: 2017-08-22
XIDIAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This type of method can effectively remove the noise in the image, but there will be block effects or over-smoothing.

Method used

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  • SAR image despeckling method based on texture enhancement and sparse coding
  • SAR image despeckling method based on texture enhancement and sparse coding
  • SAR image despeckling method based on texture enhancement and sparse coding

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Embodiment 1

[0034] Images will be polluted by different noises in the process of acquisition, storage, transmission, etc., resulting in the degradation of image quality. Therefore, in image processing, image denoising is the premise of image edge detection, pattern recognition, image segmentation, feature extraction and so on. Synthetic aperture radar technology is a major breakthrough in remote sensing technology. Its all-day and all-weather imaging capability has attracted much attention from the beginning of its research and development, and has now become the main means of earth observation. However, how to efficiently and accurately despeckle SAR images is still an urgent problem to be solved.

[0035] Image denoising methods in recent years, including SAR image speckle reduction methods, mainly complete image denoising by establishing different sparse models, and then using dictionary learning methods to update dictionaries and sparse coefficients. This kind of method can effective...

Embodiment 2

[0058] The speckle reduction method for SAR images based on texture enhancement and sparse coding is the same as that in Embodiment 1. Variance to additive noise n in step (2b) of the present invention To estimate, proceed as follows:

[0059]

[0060] in, is D n Variance. D y In order to obtain the original image coefficients after the directional wave transform of the SAR image, D n is the noise figure obtained after the directional wave transform of the SAR image, μ y =E[y], is the expectation of the original SAR image, C F is the normalized standard deviation of the noise, Ψ j defined as:

[0061]

[0062] Among them, h is the high-pass filter, g is the low-pass filter, p is the superposition number of the high-pass filter, and takes the value of 3, and l is the superposition number of the low-pass filter, which takes the value of 3 and the decomposition scale is j.

[0063] After the present invention estimates the noise variance in the directional wave d...

Embodiment 3

[0065] The SAR image speckle reduction method based on texture enhancement and sparse coding is the same as that in Embodiment 1-2. The gradient histogram h of the k-th type region in the estimated clean image x described in step (3b) r,k , follow the steps below:

[0066]

[0067] where h r,k is the estimated value of the gradient histogram of the k-th region in the clean image x, h y,k is the gradient histogram of the kth region in the original SAR image y, c is a constant, R(h x,k )Yes The gradient histogram h of the k-th region in x,k a priori regularization term for , and assuming a gradient map The pixels in are independent and identically distributed, is the gradient operation; h ε,k is the histogram of the kth class region in ε, σ 2 is the variance of the noise. represents the convolution operator.

[0068] In the present invention, the step of estimating the gradient histogram of the clean image x is performed before the speckle reduction process is...

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Abstract

The invention discloses a SAR image despeckling method based on texture enhancement and sparse coding, and solves the problem of failure of effective reservation of detail information including point targets, edges and textures etc. of an image during SAR image despeckling. The method includes: inputting an image; estimating a noise variance of the SAR image and a gradient histogram of a clear image; extracting a similar image block set and calculating a corresponding dictionary; obtaining a despeckled target function with the combination of a Gaussian scale model by employing sparse coding; updating parameters of the target function; reconstructing an image block matrix; reconstructing the image by employing a weight average method; obtaining a final image by enabling the gradient histograms of the reconstructed image and the clear image to be close to the maximum as the constraint; and outputting the final despeckled image. According to the method, the speckle noise in the SAR image can be well suppressed, a uniform area can be very smooth, the detail information including the important point targets, the edges and the textures etc. can be effectively reserved, and the method can be applied to despeckling processing of the images before processing and analysis of the SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to SAR image speckle reduction technology, and further relates to a SAR image speckle reduction method based on texture enhancement and sparse coding. It can be applied to speckle reduction before SAR image processing and analysis. Background technique [0002] Image denoising, also known as image filtering, is a type of image restoration. The purpose of image denoising is to improve a given noisy image and solve the problem of image quality degradation caused by some noise interference in the actual image. Compared with image enhancement, image denoising is an objective process. Through image denoising, the quality of the image will be significantly improved, and the image details carried by the original image will be better represented. Image denoising is a very important preprocessing method, which lays a good foundation for subsequent digital image processing. [0003] In ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06T5/40
CPCG06T5/40G06T5/70
Inventor 白静孙衍超李亚龙焦李成侯彪王爽
Owner XIDIAN UNIV
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