A face image super-resolution method with the amalgamation of global characteristics and local details information

A face image and super-resolution technology, applied in the field of face image super-resolution, can solve the problems of difficult identification, low video resolution, lack of detailed features, etc.

Inactive Publication Date: 2008-07-09
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, this method only considers the global image features, while ignoring the local detail information, resulting in the synthetic image being unclear in the local area and lacking detail features. This work appeared in the 2005 IEEE Transactions (IEEE Trans.on Systems , Man, and Cybernetics, Part-C.2005, 35(3): 425~434)
[0003] In video surveillance applications, due to the low resolution of the video and the fact that the face is too far away from the lens, the image resolution of the face part is too low, and the recognition is too poor, which makes it difficult to identify people.

Method used

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  • A face image super-resolution method with the amalgamation of global characteristics and local details information
  • A face image super-resolution method with the amalgamation of global characteristics and local details information
  • A face image super-resolution method with the amalgamation of global characteristics and local details information

Examples

Experimental program
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Effect test

Embodiment 1

[0077] Example of super-resolution of frontal unoccluded face images in the database:

[0078] The goal is to generate a corresponding high-resolution face from a low-resolution frontal face image with a neutral expression. Among the 107 volunteers in the database, 75 without glasses were selected, and their frontal face images were used as the experimental data set, of which 60 images were used to synthesize the sample set, and 15 images were used as test data. First, 60 high-resolution face images of 96×128 are down-sampled to 24×32, and these 60 high-low resolution face image pairs are used as sample data.

[0079]In the local preserving mapping algorithm, the number of transformation vectors is more critical than the size of the neighborhood. Here, the size of the neighborhood is fixed at 30, and the number of transformation vectors is set at 50. In the kNN search of the residual small block synthesis algorithm, the number of neighboring small blocks is also set to 30. W...

Embodiment 2

[0081] Super-resolution example of an actual captured image:

[0082] In order to further verify the effect of the method described in the present invention, we perform super-resolution on real-shot images. Still using the 60 high-low resolution face image pairs described in Example 1 as sample data, the number of transformation vectors in the local preserving mapping algorithm is 50, and the neighborhood size is 30. When compositing image residual blocks, the size of low-resolution small blocks is 3×3, the size of high-resolution small blocks is 12×12, and the number of adjacent small blocks is also set to 30.

[0083] Figure 7(a) is a low-resolution face image taken with a mobile phone in a stadium. The face area is manually extracted from this image and super-resolution is performed with different methods. The result is shown in Figure 7(b). From left to right, the three images in Fig. 7(b) are the original low-resolution image, the result of cubic B-spline interpolation, ...

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Abstract

The invention discloses a face mage super-resolution method which fuses global features and local detail information. The invention can synthesize a high-resolution face image according to a low-resolution face image based on a sample image. Firstly, a local maintaining mapping algorithm and a radial basic function return algorithm are combined together to get a global high-resolution face image; then a neighborhood reconstruction method is adopted to synthesize a high-resolution face residual image block and consequently form a high-resolution face residual image by combination; finally, the high-resolution face residual image is overlapped to the high-resolution face image to obtain a final super-resolution effect. The technology provided by the invention can synthesize the clearer high-resolution face image, improve the recognition of the face image and have important application significances on video monitoring, face recognition and other aspects.

Description

technical field [0001] The invention relates to digital image processing, in particular to a face image super-resolution method for fusing global features and local detail information. Background technique [0002] Face super-resolution technology is a special kind of image super-resolution technology. The current image super-resolution technology can be roughly divided into two categories, namely, image super-resolution based on reconstruction and image super-resolution based on learning. The latter is more effective than the former. In recent years, some representative learning-based image super-resolution techniques have emerged. The main idea of ​​these methods is to perform image super-resolution based on a sample image library containing pairs of high-resolution and low-resolution images. Freeman et al. proposed a sample-based method. They learned the relationship between low-resolution images and corresponding high-resolution images through Markov Networks, and used ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
Inventor 庄越挺张剑肖俊吴飞
Owner ZHEJIANG UNIV
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