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Image deblurring method based on fuzzy region segmentation

A technology of blurring areas and blurring images, which is applied in the field of image processing and can solve problems such as time-consuming and low efficiency

Inactive Publication Date: 2017-10-24
SHANTOU UNIV
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to provide an image deblurring method based on blurred area segmentation, to solve the problem that it takes a lot of time and low efficiency to perform deblurring processing on partially blurred images in the prior art

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  • Image deblurring method based on fuzzy region segmentation
  • Image deblurring method based on fuzzy region segmentation
  • Image deblurring method based on fuzzy region segmentation

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Embodiment Construction

[0064] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0065] Process flow of the present invention such as figure 1 shown, including the following steps:

[0066] S1 is the characteristic difference, and the peak measure is used to distinguish the fuzzy area B from the non-fuzzy area U;

[0067] S2 uses the Graph-cut algorithm to divide the fuzzy area B and the non-fuzzy area U;

[0068] S3 estimates the original image area L on the blurred area B;

[0069] S4 estimates the blur kernel k for the blur area B;

[0070] S5 performs image deconvolution on the blurred area B to obtain a clear area;

[0071] S6 re-merges the clear area and the unblurred area U to obtain the final deblurring result.

[0072] Embodiment adopts the present invention to figure 2 Perform deblurring.

[0073] (1) Natural images obey the heavy-tailed distribution. In general, blurred regions generally do not contain sharp edge...

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Abstract

The invention relates to an image deblurring method based on fuzzy region segmentation, and the method comprises the following steps: S1, taking a peak measure to distinguish a fuzzy region B and a non-fuzzy region U so as to represent the feature difference; S2, segmenting the fuzzy region B and the non-fuzzy region U through a Graph-cut algorithm; S3, carrying out the estimation of an original image region L for the fuzzy region B; S4, carrying out the estimation of a fuzzy kernel k of the fuzzy region B; S5, carrying out the image deconvolution of the fuzzy region B, and obtaining a clear region; S6, carrying out the fusion of the clear region and the non-fuzzy region U again, and obtaining a final deblurring result. According to the invention, the fuzzy region and the non-fuzzy region of a fuzzy image are segmented, and the estimation of the original image region and the fuzzy kernel for the fuzzy region is carried out and then the image deconvolution is carried out, thereby achieving the deblurring of the fuzzy region and obtaining the clear region. The clear region is combined with the former non-fuzzy region, and a final deblurring result of the fuzzy image is obtained, thereby effectively improving the efficiency and shortening the time.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image deblurring method based on blurred area segmentation. Background technique [0002] Blurred images are one type of image degradation that results in loss of detail. The reason for the blurred image is that in the imaging process, due to the limitation of external environmental conditions and the physical limitations of the imaging device itself and many other factors, it is inevitable that the captured image will appear blurred. The key to motion blurred image restoration is to find the degradation model of the image and take the inverse process to solve the original image. This process is called the deblurring process of the blurred image. Deblurring methods can be divided into non-blind source image deconvolution methods and image blind deconvolution methods. The non-blind source image deconvolution method restores the blurred image under the assumption that the blur ...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T7/11
CPCG06T7/11G06T2207/20056G06T5/73
Inventor 闫敬文谢婷婷彭鸿陈晓鹏
Owner SHANTOU UNIV
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