Research on Colorization of Qinghai Farmer Painting Image Based on Generative Adversarial Networks

Abstract

At present, deep learning method is widely used in the field of gray image colorization. Qinghai farmer painting has distinct national characteristics. The farmer painting has bright colors, high saturation, chaotic color distribution and low color contrast, so it is difficult to restore the image color with high fidelity by using the general deep learning image colorization method. The Pix2Pix generation adversarial network of grayscale image colorization method uses the Leaky ReLU function as the activation function. The proposal algorithm replaces the maximum pooling layer with the convolution layer to retain more image feature information and further to improve the color simulation effect. Meanwhile, in view of the lack of relevant Qinghai farmer painting data set, the data set of Qinghai farmer paintings is constructed to meet the needs of network training. The experimental results show that the improved method further improves the color effect and can generate high quality color images of Qinghai farmer paintings with more real color distribution.