Time-varying Image Filtering Method with Nonlocal Mean Spatial Domain

A technology of non-local mean and image filtering, applied in the field of image processing, it can solve the problems of limiting the application of non-local mean filter and long calculation time.

Inactive Publication Date: 2011-12-07
XIDIAN UNIV
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Problems solved by technology

This huge computational complexity causes the calculation time to be too long, which greatly limits the application of non-local mean filter. For example, the non-local mean filter method of this computational complexity cannot be applied in real-time embedded systems.

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  • Time-varying Image Filtering Method with Nonlocal Mean Spatial Domain
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  • Time-varying Image Filtering Method with Nonlocal Mean Spatial Domain

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Embodiment Construction

[0071] see figure 2 , a kind of non-local mean spatial domain time-varying image filtering method that technical scheme 1 of the present invention proposes, comprises the following steps:

[0072] Step 1: Get the original image information, set the filtering parameters, and initialize the non-normalized filtered image And weight normalization coefficient α(x, y).

[0073] The filter-related parameters set include search domain shape, matching window size, weighted kernel width h and similarity comparison norm parameter p; the search domain is a rectangle, triangle, circle or any polygon, and the matching window shape is a rectangle, set in The weighted kernel width h and the similarity comparison norm parameter p when calculating the weighted kernel weight according to the image bias similarity function value; the weighted kernel width h is any positive real number, and the similarity comparison norm parameter p is any non-negative real number ;The value of the unnormalize...

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Abstract

The invention discloses a non-local mean spatial domain time-varying image filtering method, which mainly solves the problems of large calculation amount and limited application range of the existing non-local mean spatial domain time-varying filter. The filtering process is: initialize the weight normalization coefficient at all points of the original image and the value after non-normalization filtering to 0; for each coordinate offset in the search domain, all the pixels of the original image are unified Preprocessing, and then quickly calculate the weighted kernel weights of all pixels in the original image at the coordinates of the search domain; according to the weighted kernel weights, update the weight normalization coefficient and the non-normalized filtered value; according to the weight The normalized coefficients and unnormalized filtered values ​​are calculated to obtain the filtered image. The invention greatly reduces the computational complexity of the existing time-varying filtering method in the non-local mean value space, and can be applied to the fields of image restoration, image denoising, and image super-resolution reconstruction.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an image filtering method, in particular to a time-varying image filtering method in non-local mean value space, which is used for processing digital images and video images. technical background [0002] With the development of information and multimedia technology, the application of digital image processing technology is more and more extensive. Spatial filtering technology is one of the basic methods of digital image processing, and has a wide range of applications in the field of digital images, such as image restoration, image denoising, image super-resolution reconstruction, etc. The basic idea of ​​spatial filtering is to use the relationship between adjacent pixels to obtain the corresponding processing purpose. The shape composed of adjacent pixels is called the search domain. For any pixel in the image, the filtered value of this point is equal to the normalized weighte...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/10
Inventor 齐飞付莹石光明韩钧宇张犁吴家骥
Owner XIDIAN UNIV
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