Image super-resolution method based on multi-output least square support vector regression

Abstract

The invention discloses an image super-resolution method based on multi-output least square support vector regression. The problem that existing images are low in resolution is mainly solved. The image super-resolution method includes the implementation steps of (1) building a sample base of HR luminance images and LR luminance images, and creating an LR image matrix and an HR image matrix, (2) creating a three-time amplified training set and a three-time amplified testing set according to the image matrixes, (3) training a multi-output least square support vector regression forecasting model through the training set, (4) estimating the HR luminance images of the LR image matrix in the testing set through the least square support vector regression forecasting model, and (5) updating the estimated HR luminance images through image self-similarity, and obtaining final HR images. According to the image super-resolution method, the operating time is short, the image resolution can be effectively improved, and the image super-resolution method can be used for improving the quality of satellite images and the imaging quality of high definition televisions.

Description

technical field [0001] The invention belongs to the field of image processing, and specifically relates to a method for improving image resolution, which can be used to improve the imaging quality of satellite images and high-definition televisions. Background technique [0002] Since the 1970s, charge-coupled devices (CCDs) and complementary metal-oxide-semiconductor (CMOS) image sensors have been widely used to capture digital images. Although these sensors are suitable for most image applications, the existing resolution levels and expensive hardware costs cannot meet people's needs, and it is necessary to find ways to increase the current image resolution. [0003] A promising approach is to use signal processing techniques to obtain high-resolution HR images or sequences from observed multi-frame low-resolution LR images, called image super-resolution SR reconstruction. Image super-resolution technology can reconstruct the lost information outside the cut-off frequency...

AI Technical Summary

Problems solved by technology

However, Ni et al. divided m high-resolution points corresponding to a low-resolution point into m independent tasks when building a regression model, so that a single high-resolution pixel was used as an output, resulting in difficulty in reconstructing pixels. maintain consistency

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