A Perceptually Optimized and Self-Calibrated Tone Mapping Operator

Understand

With the increasing popularity and accessibility of high dynamic range (HDR) photography, tone mapping operators (TMOs) for dynamic range compression are practically demanding.

• In this paper, we develop a two-stage neural network-based TMO that is self-calibrated and perceptually optimized.
• In Stage one, motivated by the physiology of the early stages of the human visual system, we first decompose an HDR image into a normalized Laplacian pyramid.
• We then use two lightweight deep neural networks (DNNs), taking the normalized representation as input and estimating the Laplacian pyramid of the corresponding LDR image.