Domain Adaptation

Domain adaptation techniques play a crucial role in addressing the challenges of applying machine learning models to real-world scenarios where the training and test data distributions may differ. This becomes particularly significant in low-level image processing tasks, such as RAW image enhancement, where the goal is to improve the visual quality of images captured by different camera devices. In the case of adapting a ML model for RAW image enhancement from a Canon camera to a Sony camera, domain adaptation techniques become essential to bridge the gap between the two camera-specific image distributions. By leveraging domain adaptation methods, it becomes possible to transfer the knowledge learned from the source domain (Canon camera) to the target domain (Sony camera), enabling the ML model to effectively enhance RAW images from the Sony camera. This adaptation process ensures that the ML model is robust and can produce high-quality results, tailored specifically to the characteristics and nuances of the target camera. The importance of domain adaptation techniques in low-level image processing tasks lies in their ability to overcome domain shift, improve generalization, and enable the seamless transfer of knowledge across different camera platforms, ultimately enhancing the overall performance and usability of ML-based image enhancement systems.

Reference

1. K Ram Prabhakar et al., Few-Shot Cross-Sensor Domain Adaptation between SAR and Multispectral Data, IEEE IGARSS 2022.
2. K Ram Prabhakar et al., Improving SAR and Optical Image Fusion for LULC Classification with Domain Knowledge, IEEE IGARSS 2022.
3. K Ram Prabhakar et al., Few-Shot Domain Adaptation for Low Light RAW Image Enhancement, BMVC 2021.
4. V Vinod et al., Multi-Domain Conditional Image Translation: Translating Driving Datasets from Clear-Weather to Adverse Conditions, ICCVW 2021.