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Method for reestablishment of single frame image quick super-resolution based on nucleus regression

A technology of super-resolution reconstruction and kernel regression, which is applied in image enhancement, image data processing, 2D image generation, etc., can solve the problems of long time consumption and large amount of calculation, etc., so as to improve processing speed, save processing speed, and achieve good Effects of adaptability and nonlinear processing capabilities

Inactive Publication Date: 2008-07-23
江苏美梵生物科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to provide a fast super-resolution reconstruction method for a single-frame image based on kernel regression, to overcome the defects of the existing super-resolution reconstruction method for a single-frame image with kernel regression, which has a huge amount of calculation and takes a long time

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  • Method for reestablishment of single frame image quick super-resolution based on nucleus regression
  • Method for reestablishment of single frame image quick super-resolution based on nucleus regression
  • Method for reestablishment of single frame image quick super-resolution based on nucleus regression

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specific Embodiment approach 1

[0007] Specific Embodiment 1: The present embodiment will be specifically described below with reference to FIG. 1 , FIG. 2 , FIG. 6 and FIG. 7 . This embodiment includes the following steps: 1. Map the pixel points on the low-resolution image to the high-resolution grid, and make the above-mentioned pixel points be located on the grid intersection of the high-resolution grid; 2. In the high-resolution grid, the grid intersections other than the grid intersections occupied by the low-resolution image pixels are shaved as the pixel points to be evaluated, and according to the spatial position relationship, the pixel points to be evaluated are further divided into two categories, the first One class of pixels to be evaluated is the remaining pixels to be evaluated after removing the points on the connection line between the pixels of the low-resolution image in the intersection points of the high-resolution grid; the second type of pixels to be evaluated is the pixels to be eval...

specific Embodiment approach 2

[0036] Specific Embodiment 2: The present embodiment will be specifically described below with reference to FIG. 5 and FIG. 6 . The difference between this embodiment and Embodiment 1 is that in step 3, the square neighborhood pixel set of the first type of pixels to be evaluated is determined as follows: a. Use a square local window and determine the size of the local window as n×n, n×n is the number of pixels in the low-resolution image in the local window, n is equal to 4 or 8, choosing an even window of 4×4 can achieve a better reconstruction result, and the operation speed is fast; b. The pixel is located in the center of the local window; c. All the pixels of the low-resolution image in the local window form a local neighborhood pixel set. Taking Figure 5 as an example, when the pixel to be evaluated is X1, under the 4×4 window, the local neighborhood pixel set should be {A1, A2, A3, A4, B1, B2, B3, B4, C1, C2, C3, C4, D1, D2, D3, D4}; if the pixels to be evaluated are ...

specific Embodiment approach 3

[0037] Specific Embodiment Three: The present embodiment will be specifically described below in conjunction with FIG. 7 . The difference between this embodiment and Embodiment 1 is that in step 4, the diamond-shaped neighborhood pixel set of the second type of pixel points to be evaluated is determined as follows: a. Use a diamond-shaped local window and determine the size of the local window as m×m, m is equal to 4 or 8, and m is the sum of the number of the first type of pixels to be evaluated and the number of low-resolution image pixels contained in one side of the rhombus. Taking Figure 6 as an example, when the pixel point to be evaluated is X2, under the 4×4 window, the local neighborhood pixel set is the intersection point of the thick dotted line in the figure, that is, the hollow circle point and the hollow triangle point; it should be noted that Here the hollow triangle points belong to the first type of points whose estimated values ​​have been obtained previously...

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Abstract

A fast super-resolution reconstruction method is based on kernel regression for single frame images, and the invention relates to an image super-resolution reconstruction method, which overcomes the shortages that the existing super-resolution reconstruction method of the single frame images of kernel regression is large in calculated amount and long in consuming time. The invention comprises steps as follows: mapping the pixels on the low-resolution image to high-resolution grids, confirming the pixels which are needed to be estimated and classifying the pixels into two types, confirming quadrate neighborhood pixel aggregates of each pixel which is needed to be estimated in the first type and introducing the aggregates to a kernel regression equation to calculate the pixel value, conforming rhombic neighborhood pixel aggregates of the pixels needed to be estimated in the second type and introducing the aggregates to the kernel regression equation to calculate the pixel value and outputting images when all the pixels which are needed to be estimated are value-assigned. The invention introduces two-dimension nonlinearity kernel regression to estimate interpolation points, employs local neighborhood operation to replace whole image processing, and employs immediate updating strategy, thereby realizing the super-resolution reconstruction of the single frame images.

Description

technical field [0001] The invention relates to a method for image super-resolution reconstruction. Background technique [0002] Spatial resolution is a measure of the imaging system's ability to distinguish image details, and it is also an indicator of the subtlety of objects in the image. However, in the process of image acquisition, many factors will lead to the decline or degradation of image quality. An effective way to solve this problem is super-resolution image reconstruction. Super-resolution image reconstruction is an image processing method that has emerged in recent years. This method obtains high-resolution images by estimating and integrating image information. It is an economical and easy-to-implement image reconstruction and resolution enhancement method. . The image super-resolution reconstruction technology uses the two-dimensional sampling values ​​of the degraded low-resolution image to undergo a series of two-dimensional operations to improve its res...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T11/00
Inventor 谷延锋张晔
Owner 江苏美梵生物科技有限公司
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