Design of Automatic Colouring of Sketch Images Based on Deep Learning Algorithm

In this paper, we have designed an innovative deep learning model using U-net with PatchGAN discriminator aiming at automatic colouring of sketch images, which provides a novel solution for sketch colouring. Firstly, the image is passed through the dataset to the generator, which receives the input image and tries to convert it into the expected output image. Subsequently, in each iteration, the loss functions of the generator and the discriminator are calculated and the model parameters are updated by the optimiser. Finally, predictions are made on the test dataset and the results are plotted. By passing new unseen data to the trained generator, it was observed that the generation results were as expected and the deep learning coloured images were very close to the actual coloured images. This research brings important implications for the fields of digital art and computer vision. Firstly, it provides a completely new approach to processing sketch images, opening up more possibilities for artists and designers. Second, the model expands new ideas and examples for the application of deep learning techniques in the field of image processing. Finally, in practical applications, this auto-colouring technique is expected to greatly improve productivity and can be widely used in animation, comics and other fields.