Building a High Dynamic Range Video sensor with spatially non-regular optical filtering

Although we observe a steady progress in the development of High Dynamic Range Video (HDRV) technology, current image sensors are still lacking achievable dynamic range for high image quality applications. We propose a new imaging principle that is based on a spatial variation of optical Neutral Density (ND) filters on top of some pixels. In existing work, this method has been used to trade spatial resolution for an increase in dynamic range. We improve this approach by a non-regular placement of these filters. The non-regular sampling is an important step as any sub-sampling with regular patterns leads to aliasing. The non-regular patterns however preserve just a single dominant spatial frequency and enable an image reconstruction without aliasing. In combination with a new image reconstruction approach, we are able to recover image details at high resolution. The iterative reconstruction is based on the assumption that natural images can be represented with few coeffi cients in the Fourier domain. As typical natural images can be classified as near-sparse, the method enables the reconstruction of images of high objective and visual quality. In extension to theory and simulation of this method we want to present details on a practical implementation of our method. While building a demonstration system we encountered many challenges. This includes effects like crosstalk, aspects like sensor selection and mask fabrication as well as mounting of the masks.