Enabling RAW Image Classification Using Existing RGB Classifiers
Rasmus Munksø, Mathias Andersen, Lau Nørgaard, Andreas Møgelmose, Thomas Baltzer Moeslund · 2024
Unprocessed RAW data stands out as a highly valuable image format in image editing and computer vision due to it preserving more details, colors, and a wider dynamic range as captured directly from the camera’s sensor compared to non-linearly processed RGB images. Despite its advantages, the computer vision community has largely overlooked RAW files, especially in domains where preserving precise details and accurate colors are crucial. This work addresses this oversight by leveraging transfer learning techniques. By exploiting the vast amount of available RGB data, we enhance the usability of a limited RAW image dataset for image classification. Surprisingly, applying transfer learning from an RGB-trained model to a RAW dataset yields impressive performance, reducing the dataset size barrier in RAW research. These results are promising, demonstrating the potential of cross-domain transfer learning between RAW and RGB data and opening doors for further exploration in this area of research.