Neural Color Operators for Sequential Image Retouching
- URL: http://arxiv.org/abs/2207.08080v1
- Date: Sun, 17 Jul 2022 05:33:19 GMT
- Title: Neural Color Operators for Sequential Image Retouching
- Authors: Yili Wang, Xin Li, Kun Xu, Dongliang He, Qi Zhang, Fu Li, Errui Ding
- Abstract summary: We propose a novel image retouching method by modeling the retouching process as performing a sequence of newly introduced trainable neural color operators.
The neural color operator mimics the behavior of traditional color operators and learns pixelwise color transformation while its strength is controlled by a scalar.
Our method consistently achieves the best results compared with SOTA methods in both quantitative measures and visual qualities.
- Score: 62.99812889713773
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a novel image retouching method by modeling the retouching process
as performing a sequence of newly introduced trainable neural color operators.
The neural color operator mimics the behavior of traditional color operators
and learns pixelwise color transformation while its strength is controlled by a
scalar. To reflect the homomorphism property of color operators, we employ
equivariant mapping and adopt an encoder-decoder structure which maps the
non-linear color transformation to a much simpler transformation (i.e.,
translation) in a high dimensional space. The scalar strength of each neural
color operator is predicted using CNN based strength predictors by analyzing
global image statistics. Overall, our method is rather lightweight and offers
flexible controls. Experiments and user studies on public datasets show that
our method consistently achieves the best results compared with SOTA methods in
both quantitative measures and visual qualities. The code and data will be made
publicly available.
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