AddCR: A data-driven cartoon remastering
Author(s)
Author(s)
Liu, Y.
Wen, Z.
Date Issued
2023
Publisher
Springer
Journal
The Visual Computer
Volume
39
Start page
3741
End page
3753
Abstract
Old cartoon classics have the lasting power to strike the resonance and fantasies of audiences today. However, cartoon animations from earlier years suffered from noise, low resolution, and dull lackluster color due to the improper storage environment of the film materials and limitations in the manufacturing process. In this work, we propose a deep learning-based cartoon remastering application that investigates and integrates noise removal, super-resolution, and color enhancement to improve the presentation of old cartoon animations. We employ multi-task learning methods in the denoising part and color enhancement part individually to guide the model to focus on the structure lines so that the generated image retains the sharpness and color of the structure lines. We evaluate existing super-resolution methods for cartoon inputs and find the best one that can guarantee the sharpness of the structure lines and maintain the texture of images. Moreover, we propose a reference-free color enhancement method that leverages a pre-trained classifier for old and new cartoons to guide color mapping.
SFU Affiliated Publication
Yes
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