High spectral image super-resolution method based on probability generation model

Abstract

The invention discloses a multispectral and high spectral image super-resolution method based on a probability generation model. The multispectral and high spectral image super-resolution method basedon a probability generation model includes the steps: 1) establishing a data set; 2) establishing a probability generation model; 3) inputting a spectral image; 4) maximizing the joint probability distribution logarithm; and 5) obtaining a spectral image after super-resolution. The multispectral and high spectral image super-resolution method based on a probability generation model overcomes theproblem that the prior art uses the prior information of the estimated spatial transformation matrix to make the model parameters inaccurate and the shallow model have poor representation ability, sothat the model parameters of the multispectral and high spectral image super-resolution method and the super-resolution high spectral image can be more accurate, and is an efficient high spectral image super-resolution method.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a multi-spectral and hyperspectral image super-resolution method based on a probability generation model in the technical field of image super-resolution. The invention can be used for low-resolution hyperspectral images and high-resolution multi-spectral images, and generates high-resolution hyperspectral images by using image super-resolution technology. Background technique [0002] With the rapid development of sensor technology, the spectral resolution of remote sensing images has been continuously improved. The advent of hyperspectral images has become a major leap forward in the field of remote sensing. Compared with multispectral images, hyperspectral images not only have a great improvement in information richness, but also make it possible to conduct more reasonable and effective analysis and processing of spectral data in terms of processing technology....

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that this method does not take into account the prior information of the estimated space transformation matrix, which will make the obtained model parameters inaccurate, which makes this method easy to appear in the application of image super-resolution big deviation

However, the shortcomings of this method are: this method only uses a linear model to represent the image, only uses a shallow probability model to fuse the image to achieve image super-resolution, and cannot reflect all the information in the original image , so that the super-resolved hyperspectral image has a large deviation from the original image

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