Image super-resolution and coloring method, system and electronic device

Abstract

The present application relates to an image super-resolution and coloring method, system and electronic equipment. The method includes: step a: design a new network model in combination with image super-resolution and coloring, and the network model includes an encoding module and a decoding module; step b: input the low-resolution grayscale image into the network model; step c: pass the The encoding module extracts the semantic feature map of the low-resolution grayscale image, and passes the semantic feature map to the decoding module, and the decoding module superimposes the semantic feature maps of each level and outputs a high-resolution color map. This application designs a new network model for super-resolution and coloring problems, and jointly processes super-resolution and coloring tasks, directly maps low-resolution grayscale images to high-resolution color images, improves people's visual perception, and saves resources and time.

Description

technical field [0001] The present application belongs to the technical field of image processing, and in particular relates to an image super-resolution and coloring method, system and electronic equipment. Background technique [0002] In the field of image processing, image super-resolution and colorization have been two hot research areas. Image super-resolution technology refers to recovering a high-resolution image from a low-resolution image or image sequence. This technology was first proposed in the field of optics, and is now widely used in the fields of image compression, medical imaging, and remote sensing imaging. Traditional super-resolution techniques include: interpolation-based methods (such as: nearest neighbor interpolation method, bilinear interpolation method, bicubic interpolation method, etc.), reconstruction-based methods (such as: non-uniform interpolation method, iterative back-projection method, maximum Posterior probability method, etc.), etc. ...

AI Technical Summary

Problems solved by technology

[0005] To sum up, in the existing technology, for some tasks that require both super-resolution and coloring of images (such as: processing some old photos and videos), it is necessary to separately build image super-resolution and coloring. model, train two network models, and process these two tasks separately, which is a waste of resources and time

At the same time, regardless of super-resolution first and then coloring, or first coloring and then super-resolution, due to the error produced by the previous task, it is very difficult for the latter task, which is not conducive to recovering high-resolution color images.

Images