Summary of Adaptive Domain Learning For Cross-domain Image Denoising, by Zian Qian et al.
Adaptive Domain Learning for Cross-domain Image Denoising
by Zian Qian, Chenyang Qi, Ka Lung Law, Hao Fu, Chenyang Lei, Qifeng Chen
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 adaptive domain learning (ADL) scheme addresses the challenge of cross-domain RAW image denoising by leveraging existing data from different sensors and a small amount of new sensor data. The ADL training scheme eliminates harmful source domain data that negatively impact model performance due to domain gaps, while also incorporating a modulation module that incorporates sensor-specific information for input data understanding. Experimental results on public datasets with various smartphone and DSLR cameras demonstrate the proposed model’s superiority in cross-domain image denoising, even when only a small amount of target domain sensor data is available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to remove noise from images taken by different camera sensors. Normally, an image denoising model trained on one type of sensor doesn’t work well with another type of sensor. To solve this problem, they came up with a technique called adaptive domain learning (ADL). ADL uses data from many different sensors, plus a small amount of new sensor data, to train the model. The team also created a special module that helps the model understand the input images better by taking into account the type of camera and how bright or dark the scene is. They tested their method on public datasets with various smartphone and DSLR cameras and found that it outperforms previous methods in removing noise from cross-domain images. |
Keywords
» Artificial intelligence » Image denoising
