An image interpolation method and device

Abstract

The invention provides an image interpolation method and device, and the method and device are used for reducing the sawtooth phenomena of the edges of an image under the condition that an image processor can carry out the operation easily, and optimizes the display of the image details. The method comprises the steps: determining a gradient amplitude of a pixel according to the brightness value of the pixel in an original image; determining an angle section, which is used for determining the interpolation direction and is corresponding to the gradient amplitude of the pixel, according to a preset mapping table and the gradient amplitude of the pixel, wherein the mapping table comprises the mapping relation between the gradient amplitude and the angle section; carrying out the interpolation in the horizontal direction and an edge direction according to the brightness value of the pixel in the original image, determining the brightness value of a new pixel formed through the stretching of the pixel of the original image, and obtaining a final processed image, wherein the edge direction is determined according to the angle section and the mapping relation between a preset angle section and the edge direction.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image interpolation method and device. Background technique [0002] With the emergence and wide application of digital images, people's requirements for image quality are getting higher and higher. When the hardware capabilities of image acquisition equipment are approaching the limit, using software technology to improve the original image resolution has gradually become a It is one of the hot issues in the field of digital image processing. [0003] Image interpolation is one of the traditional methods used to improve image resolution. Traditional digital image super-resolution interpolation enlargement methods can be roughly divided into three categories. First, edge-oriented interpolation is divided into explicit edge-oriented interpolation and implicit edge-oriented interpolation. This method has low computational complexity and is easy to implement i...

AI Technical Summary

Problems solved by technology

This method has low computational complexity and is easy to implement in hardware, but due to the fixed interpolation filter kernel coefficients, it is impossible to optimize more details; secondly, image super-resolution reconstruction (Super Resolution, SR) refers to the use of computer A low-resolution image (Low Resolution, LR) or image sequence is processed to restore a high-resolution image (High Resolution, HR) of an image processing technique

Although this method is better than the first method, it has high computational complexity and requires a strong continuity between the previous frame image and the next frame image; third, the matching of modules based on the database, the method requires The data volume of the image sample is large and takes up a lot of cache

Therefore, through the existing super-resolution interpolation technology, it is impossible to improve the clarity of the processed image and slow down the jagged phenomenon of the image edge

[0004] To sum up, the existing super-resolution interpolation and enlarging methods of digital images cannot reduce the jaggedness of image edges on the premise that it is easy for image processors to implement.

Images