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Image fusion effect evaluation method based on convolutional neural network

A convolutional neural network and image fusion technology, applied in the fields of computer vision, image stitching and image quality evaluation, can solve the problems of inability to accurately evaluate the stitching quality of image stitching algorithms, large errors, etc.

Active Publication Date: 2017-06-30
CHANGSHA PANODUX TECH CO LTD
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Problems solved by technology

The existing evaluation methods all evaluate the splicing quality by observing the splicing seam with human eyes, but the method of human eye observation has large errors and cannot accurately evaluate the splicing quality of the image stitching algorithm

Method used

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  • Image fusion effect evaluation method based on convolutional neural network
  • Image fusion effect evaluation method based on convolutional neural network
  • Image fusion effect evaluation method based on convolutional neural network

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Embodiment Construction

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0053] Such as figure 1 As shown, a method for evaluating the effect of image fusion based on a convolutional neural network proposed by the present invention specifically includes the following steps:

[0054] S1: Generate training data set and test data set.

[0055] Such as figure 2 As shown, the calculation method of the training data set and the test data set is as follows:

[0056] S101: Acquire a spliced ​​composite image.

[0057] Use ima...

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Abstract

The invention discloses an image fusion effect evaluation method based on a convolutional neural network, wherein the method belongs to the field of image splicing and image quality evaluation technology and relates to the field of computer vision. The method comprises the following steps of S1, generating a training data set and a testing data set; S2, generating a convolutional neural network model; and S3, testing the testing data set based on the trained convolutional neural network. According to the image fusion effect evaluation method, the convolutional neural network can be used for replacing a large amount of complicated artificial statistics and scoring, and furthermore the fusion effect in image splicing can be accurately determined, thereby overcoming restriction caused by a single factor evaluation index, facilitating realization of a full-automatic adaptive image splicing system and realizing high application value.

Description

technical field [0001] The invention belongs to the technical field of image splicing and image quality evaluation, relates to the field of computer vision, and in particular to an image fusion effect evaluation method based on a convolutional neural network. Background technique [0002] With the development of the electronic information industry and technological progress, equipment that can acquire and record video information is becoming more and more popular. However, compared with the field of view of the human eye, the field of view of ordinary cameras is much smaller. How to effectively use computer technology to expand The range of the field of view of the camera to capture images and videos has attracted extensive attention from researchers. Image stitching technology can solve the problem that wide-field images cannot be generated due to the limitation of the viewing angle and size of imaging instruments such as cameras. There are two main solutions for existing i...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0002G06T2207/20081G06T2207/20084G06T2207/30168
Inventor 不公告发明人
Owner CHANGSHA PANODUX TECH CO LTD
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