Method for restoring non-local images by combining GMRF priori

A repair method, a non-local technology, applied in the field of image repair, can solve the problems of poor connection of detailed textures and inaccurate calculation of similarity weights, etc., to achieve improved repair effects, clear detailed textures, and easy-to-implement effects

Inactive Publication Date: 2011-02-23
XIDIAN UNIV
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See Alexander Wong, Jeff Orchar, "ANonlocal-Means Approach to Exemplar-Based Inpainting", 15th IEEEInternational Conference on Image Processing, 2008, p2600-2603. This method uses a negative exponential function with a constant attenuation coefficient to calculate the relationship between the sample block and ...

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  • Method for restoring non-local images by combining GMRF priori
  • Method for restoring non-local images by combining GMRF priori
  • Method for restoring non-local images by combining GMRF priori

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[0036] Refer to attached figure 1 , the implementation steps of the present invention are as follows:

[0037] Step 1, for the image I to be repaired, determine the area Ω to be repaired and its boundary δ, and define the known area in the image as the source area Φ, that is, Φ=I-Ω, the image I to be repaired used in the present invention is as follows image 3 (b), Figure 5 (b), Figure 8 (b) shown.

[0038] Step 2, for the block ψ to be repaired with the center point p at the boundary δ p , calculate its repair priority P(p), and find out the repair block with the highest priority right Modeled using a Gaussian Random Field GMRF:

[0039] 2.1) Put the block ψ to be repaired p The repair priority of is defined as:

[0040]P(p)=C(p)D(p)1)

[0041] Among them, C(p) is the confidence item, C ( p ) = Σ q ∈ ...

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Abstract

The invention discloses a method for restoring non-local images by combining GMRF priori, which belongs to the technical field of image processing, and mainly solves the problem of inaccurate similarity weight calculation in the conventional sample-based method for restoring non-local means. The method comprises the following steps of: (1) determining an area omega to be restored and a boundary delta of the area omega to be restored for an image I to be restored; (2) calculating the priority P(p) of a block to be restored of a central point on the boundary, finding out the restored block withthe highest priority, and modeling the restored block by using GMRF; (3) searching n sample blocks which are most similar to the block to be restored in a search area, and obtaining a filling block psi p' serving as a filling block to be restored by using a GMRF-based non-local mean method; and (4) updating a confidence coefficient item and the area to be restored, and repeating the steps (1)-(4)until all points in the area to be restored are restored. The method can better connect image texture information, can make a restoration result closer to an original image in brightness, and can be used for restoring image damaged areas and removing objects in images.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to image restoration, and can be used for restoring damaged areas of photos and removing target objects in images. Background technique [0002] Image restoration is an important part of image restoration research. Its purpose is to use the existing known information in the image to repair the damaged part of the image through certain rules, so that the repaired image can be close to the original image. [0003] Existing image inpainting methods can be roughly divided into two categories: structure-based inpainting methods and texture-based inpainting methods. Among them, the structure-based repair method is essentially a partial differential equation-based repair method, which was first proposed by Bertalmio et al., see M.Bertalmil, G.Sapiro, V.Caselles, and C.Ballester, "Image Inpainting", Proceddings of Internatioanl Conferemce on Computer Graphics and Interactive Techniques,...

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Application Information

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IPC IPC(8): G06T5/00
Inventor 钟桦焦李成王婷刘芳王爽侯彪张小华缑水平
Owner XIDIAN UNIV
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