Research areas
Material Appearance and Image Analysis
Material appearance plays a central role in many domains, especially in (computational) printing, art design and visual diagnostics, where attributes as color, texture, brightness or translucency are of interest. The goal here is the analysis and prediction of such perceptual attributes from physical entities. Our objective approach combines photometry with image processing and, where appropriate, machine learning techniques. Models comprising light flux propagation and scattering are developed for this purpose.
Optical Design and Image Reconstruction
Combining imaging techniques and reconstruction algorithms extends the capabilities of conventional imaging. By working from optical system design to experimental set-ups, we can improve imaging for large telescopes, develop color lensless microscopy for microbiology, fluorescence and spectral imaging for color appearance. Thanks to an inverse problems approach, we can achieve a better reconstruction in digital holography and an improved image restauration in radar teledetection. A special focus is placed on the co-design of 3D imaging systems where the optical system and the image processing are jointly optimized.
Image analysis and understanding
The main issue addressed in this axis is image analysis and understanding ie. extracting useful information in digital images and videos and transfer it into relevant description and prediction models...