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Normalized ringing weighting based no-reference comprehensive quality assessment method for fuzzy restored image

A technology for image synthesis and quality assessment, which is applied in image enhancement, image analysis, image data processing, etc., and can solve problems such as image quality assessment for blur restoration

Active Publication Date: 2016-04-06
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the problem that the current restoration image quality evaluation method cannot comprehensively, reasonably and effectively evaluate the quality of blurred restoration images, and proposes a comprehensive quality of fuzzy restoration images based on normalized ringing weighting without reference assessment method

Method used

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  • Normalized ringing weighting based no-reference comprehensive quality assessment method for fuzzy restored image
  • Normalized ringing weighting based no-reference comprehensive quality assessment method for fuzzy restored image
  • Normalized ringing weighting based no-reference comprehensive quality assessment method for fuzzy restored image

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specific Embodiment approach 1

[0037] Specific implementation mode one: combine figure 1 A non-reference fuzzy restoration image comprehensive quality assessment method based on normalized ringing weighting in this embodiment is specifically prepared according to the following steps:

[0038] The present invention is a blurred image restoration image quality evaluation method, which can be used for the restoration image quality evaluation work of a single blurred image, and can also be used for real-time video processing to perform more effective restoration of blurred and degraded videos;

[0039] Step 1: Use a typical image restoration algorithm to restore the grayscale blurred image F(i, j) to obtain a restored image I; as shown in Figure 2(a) and Figure 2(b), Figure 3(a) and Figure 3( b), Fig. 4(a) and Fig. 4(b) are blurred image and fuzzy restoration image respectively; Wherein, F(i, j) is the i-th row and the j-th column pixel value in the grayscale fuzzy image;

[0040] Step 2: Perform secondary blu...

specific Embodiment approach 2

[0068] Embodiment 2: This embodiment differs from Embodiment 1 in that: the typical image restoration algorithm in step 1 is Tihonov regularization algorithm or full variation regularization algorithm. Other steps and parameters are the same as those in Embodiment 1.

specific Embodiment approach 3

[0069] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: the secondary fuzzy processing process in step 2 is I and G blur Perform convolution to obtain the reference image I r , the specific calculation formula is:

[0070] I r = I ⊗ G b l u r

[0071] in, I(i, j) is the pixel value of row i and column j in the restored image I; r (i, j) is the reference image I r In row i, the pixel value of column j; i=1,...,M, j=1,...,N; M are respectively the total number of rows of the gray-scale blurred image F, the total number of rows of the restored image I, and the reference image I r The total number of rows, the gradient image g I The total number of rows of the binarized image B or the gradient image g Ir the total number of rows;

[0072] N are the t...

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Abstract

The invention discloses a normalized ringing weighting based no-reference comprehensive quality assessment method for a fuzzy restored image, relates to no-reference comprehensive quality assessment methods for fuzzy restored images, and aims to solve the problem that a restored image quality assessment method cannot comprehensively, reasonably and effectively assess the quality of the fuzzy restored image. The method is implemented by the steps of 1, obtaining a restored image I; 2, obtaining a reference image Ir; 3, calculating brightness similarity metrics l(I,Ir), c(I,Ir) and s(I,Ir) of the images I and Ir; 4, calculating gradient similarity metrics of the images I and Ir; 5, obtaining MGSSIM(I,Ir); 6, obtaining an improved no-reference structural similarity metric (INRSSM); 7, obtaining a normalized ringing metric (NRM); 8, generating a restored image quality ringing degradation factor beta; and 9, obtaining a final restored image assessment metric (RIAM). The method is applied to the field of no-reference comprehensive quality assessment of fuzzy restored images.

Description

technical field [0001] The invention relates to a comprehensive quality assessment method of no-reference fuzzy restoration images, in particular to a non-reference fuzzy restoration image comprehensive quality assessment method based on normalized ringing weighting. Background technique [0002] When optical systems such as cameras and video cameras are imaging, the relative movement between the lens and the imaging scene or the defocusing of the lens will cause the captured image or video to be blurred, resulting in reduced image contrast, weakened edge and internal detail information, and affect image quality. , making it difficult for direct visual observation and digital image information processing system to accurately detect the region of interest in the image, which seriously affects the analysis and understanding of the acquired image and video information. Generally, the image quality can be improved to a certain extent through the digital blurred image restoration...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
CPCG06T7/0002G06T2207/30168
Inventor 遆晓光尹磊
Owner HARBIN INST OF TECH
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