Image super-resolution reconstruction method based on cascading linear regression

Abstract

The invention discloses an image super-resolution reconstruction method based on cascading linear regression. The image super-resolution reconstruction method mainly solves the problems that an existing method is unstable in reconstruction process and low in efficiency and too much pseudomorphism exists in generated high resolution images. The realization process comprises the following steps: (1) constructing a training image set; (2) learning training images to determine a T-group linear regression device and a T-group clustering center; (3) carrying out preprocessing on tested low resolution images to obtain initial estimated high resolution images, and extracting different components of the images; (4) blocking the brightness component, and carrying out initial estimation on the brightness characteristic block; (5) carrying out iterative updating and reconstruction on the initial estimated characteristic block to obtain high resolution image blocks; (6) combining the high resolution image blocks to obtain a high resolution brightness component image; (7) splicing the high resolution brightness component image and the chromaticity component to obtain high resolution images. The generation of pseudomorphism is reduced, the definition of the reconstructed images is improved, and the method can be used for high definition video display.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to an image super-resolution reconstruction method, and can be used in the fields of satellite remote sensing imaging, public security, high-definition video display, medical imaging, and computer identification. Background technique [0002] The image acquisition process is often affected by many factors such as atmospheric disturbances, limitations of the physical resolution of the imaging system, and scene motion changes, which often cause degradation such as optical blur, motion blur, undersampling, and noise in the actual imaging process. Due to these factors, the imaging system can only obtain images or image sequences with poor quality and low resolution, which brings many difficulties to subsequent image processing, analysis and understanding. Super-resolution reconstruction technology is to reconstruct a high-resolution clear image from a single or multiple observable lo...

AI Technical Summary

Problems solved by technology

Although the regression-based method can reduce reconstruction artifacts, the determination of the existing regression function needs to estimate too many parameters, resulting in poor generalization ability of the algorithm, and the simple regression function is difficult for high-resolution images and low-resolution images. Distinguish complex mapping relationships of images for modeling

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