Image super-resolution reconstruction method based on multi-core gaussian process regression

Abstract

The invention discloses an image super-resolution reconstruction method based on a multi-core gaussian process regression and mainly solves the problems that the current super-resolution reconstruction method generates edge sawtooth effect and the reconstruction texture is not rich. The image super-resolution reconstruction method based on the multi-core gaussian process regression comprises the following steps: (1), obtaining a low-resolution luminance image and an interpolation image and blocking the low-resolution luminance image and the interpolation image; (2), extracting central pixels and eight neighborhoods of low-resolution luminance image blocks to train an upper sampling model of the gaussian process regression; (3), forecasting pixel values of initial high-resolution luminance image blocks by using the upper sampling model; (4), combining all the initial high-resolution luminance image blocks to obtain an initial high-resolution luminance image; (5), obtaining an analog low-resolution image and blocking the analog low-resolution image; (6), extracting central pixels of the analog low-resolution image blocks to train a deblurring model of the gaussian process regression; (7), forecasting pixel values of the high-resolution luminance image blocks by using the deblurring model; and (8), combining all the high-resolution luminance image blocks to obtain a high-resolution luminance image. The image super-resolution reconstruction method based on the multi-core gaussian process regression is applicable to video monitoring and imaging of high-definition televisions.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a super-resolution reconstruction method of a single frame image based on machine learning, which can be used for video monitoring and HDTV imaging. Background technique [0002] Since the 1980s, photosensitive components represented by Charge Coupled Device (CCD) and Complementary Metal-Oxide-Semiconductor Transistor (CMOS) have been widely used in electronic imaging equipment Among them, the way for people to obtain digital images is becoming more and more simple and convenient. However, in the process of acquiring digital images, due to the limitations of electronic imaging hardware equipment and the influence of the real-time environment of the scene, it is almost impossible to obtain high-resolution images that can contain all the information of the original scene, and only blurred and noisy low-level images can be obtained. Resolving images (Low Resolution, LR), w...

AI Technical Summary

Problems solved by technology

However, it only uses the local structure information of the image itself as the training sample library, which makes the available data information limited, and the reconstruction quality will drop sharply when the amplification factor is large and the information provided by the low-resolution image is insufficient.

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