Semantic-Region Specific Lookup Tables for Image Enhancement Via Unpaired Learning
Part Of
Proceedings - International Conference on Image Processing, ICIP
Start Page
1690
End Page
1696
ISSN
15224880
ISBN
979-835034939-9
Date Issued
2024-10-27
Author(s)
DOI
10.1109/ICIP51287.2024.10647940
Abstract
This paper proposes a novel unpaired learning approach to enhance images by employing specific strategies for different semantic regions. Leveraging the generative adversarial network (GAN) framework for unpaired learning, our method incorporates a cascaded 1D and 3D lookup table (LUT) structure as the generator. Initially, context-aware 1D LUTs redistribute the input image to approach the target globally. Subsequently, category-specific 3D LUTs are merged based on the semantic category probability assigned to each pixel. The fused 3D LUTs are then applied to transform individual pixels, producing visually pleasing results. Furthermore, we introduce a semantic-attended multi-discriminator, offering more precise supervision during training. To train and evaluate our method, we have curated a semantically categorized dataset. User studies and qualitative comparisons demonstrate that our model outperforms existing methods, exhibiting better alignment with human aesthetics.
Event(s)
31st IEEE International Conference on Image Processing, ICIP 2024
Publisher
IEEE
Type
conference paper