Image super-resolution rebuilding method based on variable focal length video sequence

Abstract

The invention discloses an image super-resolution rebuilding method based on a variable focal length video sequence. The image super-resolution rebuilding method includes the following steps: a first step is that a group of low-resolution images with different focal lengths are shot to form one video sequence, all the images are changed into gray level images and subjected to image preprocessing, and reference images are selected from the gray level images; a second step is that matching point pairs between the reference images and the remaining unselected images are acquired by using the scale invariant feature transformation algorithm; a third step is that a homography matrix between the reference images and the remaining unselected images is calculated according to the matching point pairs by using the random sampling consensus algorithm; and a fourth step is that the reference images are subjected to super-resolution rebuilding by using the maximum posterior probability algorithm. Through the sub pixel precision image registration algorithm, the image super-resolution rebuilding method allows that translation, rotation, zooming and other situations exist among the images, has an outstanding effect in the super-resolution rebuilding of the variable focal length video sequence, and has certain innovativeness.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image super-resolution reconstruction method based on a zoom video sequence. Background technique [0002] With the development of communication technology and high-definition television, people have higher and higher requirements for high-quality images. However, in the process of image acquisition and processing, many factors will lead to the degradation of image quality. Things like the size of the sensor, the point spread function, and the movement of the object being photographed can blur and distort the image. On the other hand, in the process of image storage and transmission, different noises will be introduced, such as Gaussian, salt and pepper noise, etc. In compressed images, however, quantization noise is introduced. These will cause the image spectrum to overlap and degrade. [0003] The most direct way to improve the image is to increase the sensor density of ...

AI Technical Summary

Problems solved by technology

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to provide an image super-resolution reconstruction method based on a zoom video sequence in view of the fact that the traditional super-resolution reconstruction method cannot better solve the reconstruction problem of scale scaling in the low-resolution image group

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