Recovery of the Response Curve of a Digital Imaging Process by Data-centric Regularization

A method is presented that fuses multiple differently exposed images of the same static real-world scene into one single high dynamic range radiance map. Firstly, the response function of the imaging device, that maps irradiating light at the imaging sensor to gray values, is recovered. The mapping is usually not linear for 8-bit images. This nonlinearity affects image processing algorithms that do assume a linear model of light. With the response function known this compression can be reversed. For reliable recovery the whole set of images is segmented in a single step, and regions of roughly constant radiance in the scene are labeled. Underand overexposed parts in one image are segmented without loss of detail throughout the scene. From these regions and a parametrization of digital film the slope of the response curve is estimated, whereby various noise sources of an imaging sensor have been modeled. From its slope the response function is recovered and images are fused. The dynamic range of outdoor environments cannot be captured by a single image. Valuable information gets lost because of underor overexposure. A radiance map overcomes this problem and makes object recognition or visual self-localisation of robots easier.