Image amplification method

Abstract

The present invention discloses an image amplification method. The method comprises: a gradient value estimation step, wherein the gradient of a high-definition image is estimated through adoption of a non-local average filtering method; a gradient guidance interpolation step, wherein the interpolation of an edge pixel is guided through adoption of the gradient value; an interpolation result correction step, wherein the interpolation image is subjected to postprocessing through adoption of the non-local average filtering method used in the gradient estimation stage to remove the noise and the artificial effect induced by the interpolation; and a texture structure reconstruction step, wherein the interpolation image is taken as an initial result, the gradient is taken as a constraint and a final high-definition image is obtained through adoption of the reconstruction method.

Description

technical field [0001] The invention relates to the technical field of video image processing, in particular to an image enlargement method based on structure preservation. Background technique [0002] With the popularization of digital products, images, as the main source of information for human beings, have been more and more widely used. At the same time, digital image processing technology has also been developed rapidly. The acquisition of video images is a key step in the digital image processing system. During the digital acquisition process, the image resolution and image quality will decrease due to the following factors. Sampling frequency, undersampling causes the spectrum of the image to alias and degrade due to distortion effects. Atmospheric disturbances, defocusing, sensor size, and relative motion between the image capture device and the object being photographed can cause image blur. In the process of image acquisition, transmission and storage, noise,...

AI Technical Summary

Problems solved by technology

[0007] Algorithms in the prior art are based on interpolation methods, although the amount of calculation is low, the reconstruction effect is poor

Based on the reconstruction method, both the edge and the texture cannot be well reconstructed at the same time

Learning-based methods are computationally complex and strongly dependent on the choice of training library

Images