Expanding dynamic range in a single-shot image through a sparse grid of low exposure pixels
The joint work of Leon Eisemann (Student CSM), Jan Fröhlich (AM), Axel Hartz (AM) and Johannes Maucher (MI/CSM) on ML-based dynamic range expanding in single-shot images, has been presented recently at IS&T International Symposium on Electronic Imaging 2020 in San Francisco. The paper can be downloaded from here.
Camera sensors are physically restricted in the amount of luminance which can be captured at once. To achieve a higher dynamic range, multiple exposures are typically combined. This method comes with several disadvantages, like temporal or alignment aliasing. Hence, we propose a method to preserve high luminance information in a single-shot image. By introducing a grid of highlight preserving pixels, which equals 1% of the total amount of pixels, we are able to sustain information directly incamera for later processing. To provide evidence, that this number of pixels is enough for gaining additional dynamic range, we use a U-Net for reconstruction. For training, we make use of the HDR+ dataset, which we augment to simulate our proposed grid. We demonstrate that our approach can preserve high luminance information, which can be used for a visually convincing reconstruction, close to the ground truth.