  It is very challenging to reconstruct a high dynamic range (HDR) from a low
dynamic range (LDR) image as an ill-posed problem. This paper proposes a
luminance attentive network named LANet for HDR reconstruction from a single
LDR image. Our method is based on two fundamental observations: (1) HDR images
stored in relative luminance are scale-invariant, which means the HDR images
will hold the same information when multiplied by any positive real number.
Based on this observation, we propose a novel normalization method called " HDR
calibration " for HDR images stored in relative luminance, calibrating HDR
images into a similar luminance scale according to the LDR images. (2) The main
difference between HDR images and LDR images is in under-/over-exposed areas,
especially those highlighted. Following this observation, we propose a
luminance attention module with a two-stream structure for LANet to pay more
attention to the under-/over-exposed areas. In addition, we propose an extended
network called panoLANet for HDR panorama reconstruction from an LDR panorama
and build a dualnet structure for panoLANet to solve the distortion problem
caused by the equirectangular panorama. Extensive experiments show that our
proposed approach LANet can reconstruct visually convincing HDR images and
demonstrate its superiority over state-of-the-art approaches in terms of all
metrics in inverse tone mapping. The image-based lighting application with our
proposed panoLANet also demonstrates that our method can simulate natural scene
lighting using only LDR panorama. Our source code is available at
https://github.com/LWT3437/LANet.
