An image coloring method based on a self-attention generative adversarial network

Abstract

The invention discloses an image coloring method based on a self-attention generative adversarial network. The method comprises the following steps: step 1, training a grayscale image coloring model;2, inputting the gray level images in the training data set into an adversarial network to execute a feature extraction stage, a feature fusion stage, a deconvolution calculation stage and a self-attention learning stage to reconstruct corresponding color images; step 3, comparing the color image reconstructed after the self-attention learning with the corresponding original color image, and calculating a penalty function shown in the specification; and step 4, calculating a penalty function according to the formula shown in the specification, 4, taking the loss function as the optimization loss of the GAN on the basis of the formula shown in the specification, wherein the formula shown in the specification is shown in the specification; and step 5, dividing the training process into a plurality of preset sub-training periods, and sequentially training the sub-training periods by adopting a step-by-step growth strategy to obtain the generator network. According to the method, the colorimage conforming to human subjective visual preferences is reconstructed from a black-white or gray level image by adopting an adversarial generation network, so that the color image is more realistic.

Description

technical field [0001] The invention relates to the field of image coloring and enhancement, in particular to an image coloring method based on self-attention generation confrontation network. Background technique [0002] Image coloring is a basic means of image enhancement, which aims to supplement color information for grayscale images without any color hints, so as to obtain a better and more complete look and feel and visual experience. With the development of the times, color images and videos have become a common experience for ordinary consumers. Compared with the early pictures and video materials that only had black and white or grayscale information, they are more colorful. However, due to the lack of color information, the old black and white or grayscale material is extremely difficult to restore to the more experienced color material for modern audiences. Furthermore, with the leap of hardware technology, in order to obtain a better visual experience, people's...

AI Technical Summary

Problems solved by technology

However, the more common method is to use the pixel-by-pixel L1 or L2 norm to calculate the difference between the reconstructed color image and its corresponding real color image, which will inevitably lead to the problem of color blur and color overflow

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