Papers › Multiple transformation function estimation for image enhancement
Multiple transformation function estimation for image enhancement
Jaemin Park, An Gia Vien, Minhee Cha, Thuy Thi Pham, HanUl Kim, and Chul Lee
Most deep learning-based image enhancement algorithms have been developed based on the image-to-image translation approach, in which enhancement processes are difficult to interpret. In this paper, we propose a novel interpretable image enhancement algorithm that estimates multiple transformation functions to describe complex color mapping. First, we develop a histogram-based multiple transformation function estimation network (HMTF-Net) to estimate multiple transformation functions by exploiting both the spatial and statistical information of the input images. Second, we estimate pixel-wise weight maps, which indicate the contribution of each transformation function at each pixel, based on the local structures of the input image and the transformed images obtained by each transformation function. Finally, we obtain the enhanced image as the weighted sum of the transformed images using the estimated weight maps. Extensive experiments confirm the effectiveness of the proposed approach and demonstrate that the proposed algorithm outperforms state-of-the-art image enhancement algorithms for different image enhancement tasks.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Enhancement | MIT-Adobe 5k | MTFE | PSNR on proRGB | 25.46 | #7 of 11 | Archive leaderboard | report |
| Low-Light Image Enhancement | LOL | MTFE | Average PSNR | 22.86 | #33 of 40 | Archive leaderboard | report |
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