Towards reconstructing HDR light fields by combining 2D and 3D CNN architectures

High dynamic range imaging has become a technological trend in the past couple of decades, particularly through its integration into many applications. Numerous attempts were made to reconstruct HDR images from low-dynamic-range data. Such reconstruction techniques can be classified into single-camera and multi-camera approaches. Single-camera setups are less expensive, yet multi-camera setups are more efficient. At the time of this paper, there is already a great number of algorithms for single-camera HDR image reconstruction, but there are only a few for HDR video reconstruction. The latter takes into account the temporal coherence between consecutive video frames, leading to better results. For light field images, this remains a challenging open issue, as the HDR video reconstruction methods do not work as efficiently for light field images as HDR image reconstruction algorithms do. However, analogously to 2D videos, where consecutive frames have temporal coherence, many similarities can be found between the adjacent views of light field contents. In this paper, we investigate the theoretical possibilities of combining CNN architectures utilized for HDR images and videos, in order to enhance the outputs of HDR light field image reconstruction. The concept of our work is to exploit the similarities between light field images since they all visualize the same scene from different angular perspectives.