  The range of real-world scene luminance is larger than the capture capability
of many digital camera sensors which leads to details being lost in captured
images, most typically in bright regions. Inverse tone mapping attempts to
boost these captured Standard Dynamic Range (SDR) images back to High Dynamic
Range (HDR) by creating a mapping that linearizes the well exposed values from
the SDR image, and provides a luminance boost to the clipped content. However,
in most cases, the details in the clipped regions cannot be recovered or
estimated. In this paper, we present a novel inverse tone mapping approach for
mapping SDR images to HDR that generates lost details in clipped regions
through a semantic-aware diffusion based inpainting approach. Our method
proposes two major contributions - first, we propose to use a semantic graph to
guide SDR diffusion based inpainting in masked regions in a saturated image.
Second, drawing inspiration from traditional HDR imaging and bracketing
methods, we propose a principled formulation to lift the SDR inpainted regions
to HDR that is compatible with generative inpainting methods. Results show that
our method demonstrates superior performance across different datasets on
objective metrics, and subjective experiments show that the proposed method
matches (and in most cases outperforms) state-of-art inverse tone mapping
operators in terms of objective metrics and outperforms them for visual
fidelity.
