Method for evaluating clarity of stitched image

Abstract

The invention discloses a method for evaluating clarity of a stitched image, belongs to the technical field of image stitching and image quality evaluation, and relates to the field of computer vision. The method includes the following steps: S1. generating a training data set and a test data set; S2. generating a convolution neural network model; and S3. training the test data set on the basis of the trained convolution neural network. According to the invention, the method, based on an output label of each block output by the convolution neural network, computes the average value of the output labels of all the blocks which constitute a to-be-evaluated stitched image, then computing the average value of the output labels of all stitched images which are synthesized with the same stitching algorithm, the average value of the output labels of all the stitched images serving as a criterion for evaluating the effects of the stitching algorithm. The method, by means of the convolution neural network, can replace tedious and bulk amount of manual statistics and scoring, can accurately determine the clarity of a fusion region in stitching images, overcomes the limitation brought by single-factor evaluation index, can help the realization of automatic and self-adaptive image stitching system, and has significant 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 relates to a method for evaluating the clarity of spliced ​​images. 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 image stitching te...

AI Technical Summary

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

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