Super-resolution image reconstruction method and device

Abstract

The invention provides a reconstruction method and apparatus for a super-resolution image. The method comprises: N-times downsampling is carried out on an inputted low-resolution image to generate an image piece base; for any given image piece of the low-resolution image, a similar image sheet is obtained from the image piece base by using an approximate nearest neighbor algorithm; image areas corresponding to the similar image piece are aggregated and overlapped and a first high-resolution image is obtained according to weight coefficients corresponding to the image areas; and the first high-resolution image is processed by using a rear projection algorithm to obtain a second high-resolution image and the second high-resolution image is added into the image piece base. With a rear-projection-algorithm-based multi-stage amplification technology, an initial target high-resolution image is estimated; mathematical modeling is carried out by utilizing data fidelity, sparse non-local regularization and prior gradient histogram regularization; and an optimization problem is solved to obtain a final target high-resolution image. Therefore, edge and texture information of the low-resolution image can be effectively recovered and the reconstructed image quality is improved.

Description

technical field [0001] The present invention relates to image and video processing technology, in particular to a super-resolution image reconstruction method and device. Background technique [0002] With the rapid development of multimedia technology, people have higher and higher requirements for visual fidelity and rich picture detail information of images and videos, which requires high-resolution images and videos, and in the actual image processing and analysis system , often require high-resolution images and videos. However, the resolution of images is usually limited by constraints such as image acquisition equipment, optics, imaging speed, and hardware storage, and low-resolution images and videos are captured in many imaging applications, such as digital cameras, medical imaging systems And video surveillance systems capture low-resolution images and videos. Therefore, in order to obtain high-resolution images and videos, super-resolution technology is required...

AI Technical Summary

Problems solved by technology

[0004] However, for the complex structure of low-resolution images, the above super-resolution methods cannot recover tiny image edges and details, and even blurring or jagged artifacts appear in the results due to the randomness of the fine-grained image structure and noise. Moreover, the performance of the above super-resolution methods heavily depends on the noise level of the input image

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