  Mapping a single exposure low dynamic range (LDR) image into a high dynamic
range (HDR) is considered among the most strenuous image to image translation
tasks due to exposure-related missing information. This study tackles the
challenges of single-shot LDR to HDR mapping by proposing a novel two-stage
deep network. Notably, our proposed method aims to reconstruct an HDR image
without knowing hardware information, including camera response function (CRF)
and exposure settings. Therefore, we aim to perform image enhancement task like
denoising, exposure correction, etc., in the first stage. Additionally, the
second stage of our deep network learns tone mapping and bit-expansion from a
convex set of data samples. The qualitative and quantitative comparisons
demonstrate that the proposed method can outperform the existing LDR to HDR
works with a marginal difference. Apart from that, we collected an LDR image
dataset incorporating different camera systems. The evaluation with our
collected real-world LDR images illustrates that the proposed method can
reconstruct plausible HDR images without presenting any visual artefacts. Code
available: https://github. com/sharif-apu/twostageHDR_NTIRE21.
