Image super-resolution method based on line scanning

Abstract

The invention provides an image super-resolution method based on line scanning, and the method comprises the steps: obtaining a plurality of low-resolution images containing different pixel position information of a target through the line scanning in an orthogonal direction through a low-resolution detector in combination with a high-resolution spatial modulator; and the low-resolution images in the two directions are further subjected to corresponding pixel information extraction and splicing fusion to obtain image super-resolution reconstruction results in the two directions. And finally, providing a joint reconstruction method based on global and low-rank constraints to carry out fusion and stripe denoising on reconstruction results in two directions, and finally obtaining a target high-resolution reconstruction image. Image reconstruction with different super-resolution multiples is realized by adjusting the working pixels of the spatial light modulator and the pixels of the imaging area of the detector, the relationship between the sampling time and the reconstruction quality is balanced by adjusting the scanning interval, and the method has relatively high flexibility and robustness. The method provided by the invention is remarkable in resolution enhancement effect and high in competitiveness.

Description

technical field [0001] The invention belongs to the field of image super-resolution, and relates to a line-scan-based image super-resolution method, which is applicable to various image super-resolution application scenarios with limited resources. Background technique [0002] Image super-resolution reconstruction (Super-Resolution, SR) refers to recovering a high-resolution (High-Resolution, HR) image process. This technology has been widely used in various fields such as medical diagnosis, remote sensing detection, computer vision and military reconnaissance. [1,2] . [0003] Traditional image super-resolution methods include interpolation methods and learning-based methods. The earliest interpolation method used [3] Only the pixel information of the low-resolution image itself can be used to interpolate the surrounding pixels to achieve high-resolution image reconstruction, and it is difficult to obtain satisfactory results. To alleviate this problem, instance-based...

AI Technical Summary

Problems solved by technology

How to achieve fast data acquisition and high-quality result reconstruction, and how to effectively solve pixel aliasing and result inhomogeneity are key issues in image super-resolution methods based on line scan

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