A remote sensing image super-resolution reconstruction method combining a two-parameter beta process with a dictionary

Abstract

The invention provides an accurate and small-computation remote sensing image super-resolution reconstruction method combining a two-parameter beta process with a dictionary, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: S1, inputting a low-resolution image to be reconstructed, a high-resolution image dictionary D(x), a low-resolution image dictionary D(y) and a mapping matrix A; S2, performing sparse coding on the input low-resolution image according to a dictionary D (y) to obtain a low-resolution sparse coefficient, mappinga high-resolution sparse coefficient corresponding to the low-resolution sparse coefficient by using a matrix A, and reconstructing a high-resolution image by using the high-resolution sparse coefficient and a dictionary D (x); the acquisition method of the dictionary D (x), the dictionary D (y) and the matrix A comprises the steps that according to paired high-resolution and low-resolution training images, the dictionary D (x), the dictionary D (y) and the matrix A are acquired through a double-parameter beta process, the matrix A is a mapping matrix from sparse coefficients of the dictionaryD (y) to sparse coefficients of the dictionary D (x), and the sparse coefficients are products of coefficient weights and dictionary atoms.

Description

technical field [0001] The invention relates to a remote sensing image reconstruction method, in particular to a remote sensing image super-resolution reconstruction method using a double-parameter beta process joint dictionary, and belongs to the technical field of remote sensing image processing. Background technique [0002] High-resolution remote sensing images are an important guarantee for remote sensing applications, but it is not easy to obtain high-resolution remote sensing images, which will be affected by many factors such as remote sensing imaging sensor array density, atmospheric disturbance, relative motion, and environmental noise. Image spatial resolution is an important research direction of remote sensing technology. The most direct way to obtain high-resolution remote sensing images is to improve the remote sensing imaging hardware system, but it will bring defects such as high cost, long cycle, and even technical impossibility. Remote sensing image super...

AI Technical Summary

Problems solved by technology

These algorithms can generate overcomplete dictionaries and sparse coefficients, but the sparse coefficients are shared by high-resolution and low-resolution dictionaries. Therefore, it is difficult for the algorithm to fit dictionaries and coefficients in two feature spaces.

Zeyde et al. proposed a two-step dictionary learning algorithm, in which the low-resolution dictionary is learned by the K-SVD algorithm, and the high-resolution dictionary is generated by the least squares method. Although this method greatly reduces the amount of calculation, the reconstruction of high-resolution images is still Inaccurate

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