SAR image super-resolution method based on marginal information and deconvolution

Abstract

The invention provides an SAR image super-resolution method based on marginal information and deconvolution. The method achieves the aim that a low-resolution SAR image is reconstructed to be a high-resolution SAR image, and comprises the steps that the low-resolution SAR image is generated to be a result image on which super resolution is to be conducted; original SAR image pixel dot interlace evaluation is conducted; for each image pixel which is not evaluated, four image pixels are used for estimating the gray level distance, and calculating a standard deviation at the same time; the obtained gray level distance and the standard deviation are used for conducting estimation filling on the pixels which are not evaluated; the result image anticlockwise rotates by 45 degrees, and in the same way, the gray level distance is estimated, the standard deviation is calculated, and estimation filling is conducted on the pixels; then the result image anticlockwise rotates by 45 degrees, Gaussian point diffusion matrix deconvolution is conduced on the result image, and the super-resolution result image is obtained after Fourier transform is conducted on the image. According to the SAR image super-resolution method based on marginal information and deconvolution, marginal maximum a posteriori estimation is achieved, the integrality of the image content structure is ensured, fast Fourier transform deconvolution processing is adopted, and the blur caused by marginal interpolation is lowered.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a SAR image super-resolution method based on edge information and deconvolution, which can be applied to target detection and recognition. Background technique [0002] Synthetic Aperture Radar (SAR) has shown great potential and application prospects in military and national economic fields due to its all-time, all-weather, and strong penetration characteristics. Resolution is an important index to measure the quality of SAR image, which largely determines the readability and target discrimination ability of the image. The higher the resolution of the acquired SAR image, the richer the target information and the more obvious the features can be extracted, which is more conducive to the subsequent application of the SAR image. Many applications place high requirements on the resolution of SAR images. For example, in military applications, high-resolution image resources are requi...

AI Technical Summary

Problems solved by technology

Learning-based methods often require a large amount of training data. In many cases, it is difficult to obtain corresponding high-resolution images for SAR images. At the same time, learning-based methods are computationally complex and difficult to process in real time.

The methods based on reconstruction mainly include the traditional interpolation method and the method based on edge interpolation, while the traditional interpolation method performs super-resolution processing on SAR images, which will cover up a lot of detailed information and cannot accurately reconstruct the target and the region of interest; based on edge interpolation The methods, such as the NEDI method proposed by Xin Li et al. and the ICBI method proposed by Andrea Giachetti et al., although they can maintain good edge information, they also need to solve the matrix pseudo-inverse or solve the complex operation of the target optimization problem, which affects the operation of the algorithm. time, it is difficult to achieve real-time processing

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