Digital image triple interpolation amplification method by combining local direction features

Abstract

The invention discloses a digital image triple interpolation amplification method by combining local direction features. According to the method, firstly, four direction feathers with low-resolution-ratio images are extracted, and then, interpolation points are divided into three types according to the position relationship, and the interpolation is respectively carried out. For different-line different-row interpolation points, distances between known points and points to be interpolated and corresponding direction feathers are firstly used as weighting coefficients for carrying out interpolation, then, the direction feathers of the known points in the adjacent region are used for judging whether sub blocks are 45-degree and 135-degree high-texture regions or not, and if so, the corresponding interpolation points are updated in the texture direction by a linear interpolation method; and when same-line same-row points to be interpolated are evaluated, interpolation points of 0-degree and 90-degree high texture regions are subjected to interpolation in the corresponding direction, and other points are reconstructed according to the traditional linear interpolation method. The method has the advantages that edge fuzzyness and sawtooth phenomena of the interpolation images can be effectively inhabited, meanwhile, the operation time is shortened, the edges of the reconstructed images are clearer, and the visual effect is sharper.

Description

technical field [0001] The invention belongs to the technical field of image super-resolution (Super Resolution) reconstruction. Background technique [0002] Image super-resolution reconstruction technology refers to using one or more low-resolution images to reconstruct a high-resolution image by estimating the high-frequency information lost in the low-resolution images. Image super-resolution reconstruction technology is widely used in remote sensing satellite images, military reconnaissance, medical imaging, security monitoring, and traffic management, and has broad application prospects and important research value. [0003] As an important branch of image super-resolution (SR) reconstruction, interpolation magnification has the characteristics of simple implementation and fast operation speed. Traditional interpolation methods include nearest neighbor, bilinear and bicubic interpolation, etc. These methods use the distance factors from known points in the neighborhoo...

AI Technical Summary

Problems solved by technology

However, because this method needs to perform bilinear interpolation on the low-resolution image first when calculating the directional features, it not only reduces the calculation speed, but also makes the interpolated image unavoidable due to the low-pass smoothing effect of bilinear interpolation. Errors are introduced, which leads to inaccurate estimation of local texture features, which affects the accuracy of subsequent interpolation reconstruction

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