  High-dynamic-range (HDR) imaging is crucial for many computer graphics and
vision applications. Yet, acquiring HDR images with a single shot remains a
challenging problem. Whereas modern deep learning approaches are successful at
hallucinating plausible HDR content from a single low-dynamic-range (LDR)
image, saturated scene details often cannot be faithfully recovered. Inspired
by recent deep optical imaging approaches, we interpret this problem as jointly
training an optical encoder and electronic decoder where the encoder is
parameterized by the point spread function (PSF) of the lens, the bottleneck is
the sensor with a limited dynamic range, and the decoder is a convolutional
neural network (CNN). The lens surface is then jointly optimized with the CNN
in a training phase; we fabricate this optimized optical element and attach it
as a hardware add-on to a conventional camera during inference. In extensive
simulations and with a physical prototype, we demonstrate that this end-to-end
deep optical imaging approach to single-shot HDR imaging outperforms both
purely CNN-based approaches and other PSF engineering approaches.
