Image Super-resolution Reconstruction Method Based on Multilayer Support Vector Regression Machine Model

Abstract

The invention discloses an image super-resolution reconstruction method based on multi-layer supporting vectors and mainly aims at solving the problems that high-frequency information is lost and a ring effect is generated in an existing super-resolution method. The realizing steps of the method are as follows: (1) respectively establishing a training sample base and a testing sample base, (2) establishing first-layer support vector regression machine models of testing samples, (3) predicating high-resolution luminance initial images and initial training images, (4) calculating differential value training images of the initial training images, (5) establishing second-layer supporting vector regression machine models of the differential value training images, (6) calculating high-resolution luminance differential value images, (7) adding the high-resolution luminance initial images and the high-resolution luminance differential value images to obtain high-resolution luminance images, The images reconstructed through the method have the advantages of being clear in edge, rich in texture and closer to real images. The method can be used for video monitoring and high-definition television (HDTV) imaging.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an image super-resolution reconstruction method, which can be used for video monitoring and HDTV imaging. Background technique [0002] Image resolution is an important performance index to measure image quality. With the invention of CCD and CMOS image sensor, the image obtained by people is clearer, but in the process of image acquisition and processing, it is easily affected by weather conditions, physical conditions, human factors and other factors, which will degrade the image quality. Improving the hardware conditions of image imaging equipment can improve image quality, but high-density image sensors are expensive and it is almost difficult to improve the performance of sensor arrays from a technical level. In order to meet people's demand for low cost while significantly improving image resolution, super-resolution image reconstruction technology has been ...

AI Technical Summary

Problems solved by technology

However, the performance of this method depends entirely on the training sample library. If the sample selection is not good, it is difficult to achieve better image super-resolution reconstruction.

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