From RAW to sRGB and Back: Modeling the Onboard Camera Processing
Michael S. Brown
Description
Image processing and computer vision algorithms often treat a camera as a light measurement device, where pixel intensities represent meaningful physical measurements of the imaged scene. However, modern digital cameras are anything but light measuring devices, with a wide range of on-board processing, including scene relighting (dynamic light optimization), white balance, and various color rendering options (e.g. landscape, portrait, vivid). This on-board processing is often how camera manufacturers distinguish themselves among competitors, resulting in two different cameras producing noticeably different output images (sRGB) for the same scene. This raises the question if meaningful values can be obtained from camera objects. In this tutorial we will overview the camera imaging pipeline and discuss various methods that have addressed how to reverse this processing to obtain meaningful physical values from digital photographs.
Topics Covered:
Part 1: Imaging Pipeline
1) Imaging Fundamentals
- Review of camera spectral response
- Review of standard color spaces
2) Camera compensation
- Exposure Adjustment
- White balance
- Demosiaking
- Sensor characterization
4) Additional Processing
- Sharpening
- Dynamic Scene Relighting
Part 2: Color Manipulation
5) On-board Color Rendering
- Tone Mapping
- Gamut Mapping/Color Preference
6) Modeling the In-Camera Pipeline
- New model for in-camera pipeline
- Deterministic approaches
- Probabilistic approaches
- Lattice Regression for 3D LUT generation
7) Applications for Reserving sRGB to RAW
- Accurate white-balance correction
- Photo-refinishing
Biography
Michael S. Brown obtained his BS and PhD in Computer Science from the University of Kentucky in 1995 and 2001 respectively. He is currently an Associate Professor and Vice Dean (External Relations) in the School of Computing at the National University of Singapore. He has served as an area chair multiple times for CVPR, ICCV, ECCV, and ACCV and is currently an associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), the International Journal of Computer Vision (IJCV), and Computer Graphics Forum (CGF). His research interests include computer vision, image processing and computer graphics.