Summary of Taming Lookup Tables For Efficient Image Retouching, by Sidi Yang et al.
Taming Lookup Tables for Efficient Image Retouching
by Sidi Yang, Binxiao Huang, Mingdeng Cao, Yatai Ji, Hanzhong Guo, Ngai Wong, Yujiu Yang
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Image Color Enhancement Lookup Table (ICELUT) model leverages lookup tables for efficient edge inference, eliminating the need for convolutional neural networks (CNNs). ICELUT combines pointwise convolution and a split fully connected layer to extract color information and incorporate global details. The model achieves near-state-of-the-art performance while significantly reducing power consumption. It exhibits robust scalability, maintaining performance even with downsampled input images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ICELUT is a new way to improve image quality on devices like cameras, phones, and TVs. Existing methods are good at making pictures look better but use too much computer power and battery life. The researchers created a special table (LUT) that can enhance colors quickly and efficiently, without needing powerful computers or CNNs. This LUT-based approach is the first to be this fast, with results on par with the best methods. |
Keywords
» Artificial intelligence » Inference
