RGB+: Near-Infrared in Computational Photography
Full Professor, School of Information and Communication Sciences (IC)
École Polytechnique Fédérale de Lausanne (EPFL)
Conventional digital cameras exhibit a number of limitations that computational photography systems try to overcome. For example, the disambiguation of how much the illuminant(s) and the object reflectance contribute to a pixel value is mathematically ill-posed. Given how most modern cameras capture images, blur and limited depth-of-field may also introduce noise and unwanted artifacts. To solve this problem, experts have proposed modified hardware, smart algorithms using priors, and machine learning approaches. In our research, we use “extra information” in the form of near-infrared (NIR), the wavelength range adjacent to the visible spectrum and easily captured by conventional sensors. Capturing NIR can improve computational photography tasks such as dehazing, white-balancing, shadow detection, deblurring and depth-of-field extension, as well as computer vision applications such as detection and classification.