A method of image super-resolution reconstruction based on sub-pixel offset model

Abstract

The invention discloses an image super-resolution reestablishing method based on a sub pixel displacement model. First, input images are configured into a grid, and an image with pixels distributed evenly is formed; then, the sub pixel displacement model is provided, so that a new image degradation model is established; then, a displacement estimation algorithm based on gradients is designed to estimate sub pixel displacement quantity, and accordingly a displacement kernel function is established and computed; and finally, according to the new image degradation model, the displacement kernel function and a Taylor series extension rule are used for establishing a kernel regression estimation expression, and accordingly a high-resolution image is reestablished. According to the method, two independent links of interpolation resampling and restoring in kernel regression super-resolution reestablishing are integrated, and the complexity of substep modeling and the ill-posedness of deconvolution are avoided. Meanwhile, the method does not limit the number of observed images, the method can be used for single-frame reestablishing and multi-frame reestablishing, and applicability is enhanced.

Description

Technical field [0001] The invention relates to an image super-resolution reconstruction method based on a sub-pixel shift model, which belongs to the field of computer image and video processing. Background technique [0002] In recent years, high-resolution display devices, especially high-definition liquid crystal displays, have become popular. In many cases, the image or video displayed on the HD monitor is not clear. This is mainly because in the image or video acquisition link, most of the equipment used considers cost factors (mainly technology, capital, etc.), and the inherent resolution of its imaging sensor is limited, and only low-resolution images can be acquired. How to maximize the performance of high-resolution display devices (presenting low-resolution images in high-definition) without increasing the cost of imaging equipment for related applications (for example, medical imaging diagnosis, satellite remote sensing analysis, remote monitoring and research or mob...

AI Technical Summary

Problems solved by technology

[0009] Purpose of the invention: In order to solve the problem of poor deconvolution effect of the independent restoration link in kernel regression super-resolution reconstruction, the present invention provides a kernel regression super-resolution reconstruction method based on a sub-pixel offset model. The integration of interpolation resampling and restoration in super-resolution reconstruction avoids the complexity of step-by-step modeling and the ill-posedness of deconvolution

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