With the increasing popularity and accessibility of high dynamic range (HDR)
photography, tone mapping operators (TMOs) for dynamic range compression and
medium presentation are practically demanding. In this paper, we develop a
two-stage neural network-based HDR image TMO that is biologically-inspired,
computationally efficient, and perceptually optimized. In Stage one, motivated
by the physiology of the early stages of the human visual system (HVS), we
first decompose an HDR image into a normalized Laplacian pyramid. We then use
two lightweight deep neural networks (DNNs) that take this normalized
representation as input and estimate the Laplacian pyramid of the corresponding
LDR image. We optimize the tone mapping network by minimizing the normalized
Laplacian pyramid distance (NLPD), a perceptual metric calibrated against human
judgments of tone-mapped image quality. In Stage two, we generate a
pseudo-multi-exposure image stack with different color saturation and detail
visibility by inputting an HDR image ``calibrated'' with different maximum
luminances to the learned tone mapping network. We then train another
lightweight DNN to fuse the LDR image stack into a desired LDR image by
maximizing a variant of MEF-SSIM, another perceptually calibrated metric for
image fusion. By doing so, the proposed TMO is fully automatic to tone map
uncalibrated HDR images. Across an independent set of HDR images, we find that
our method produces images with consistently better visual quality, and is
among the fastest local TMOs.

本文利用基于神经网络的两阶段高动态范围图像色调映射算法，根据感知优化程度，将 HDR 图像通过对比计算 Laplacian 金字塔损失度来自动生成相应的 LDR 图像堆栈，并融合生成期望的 LDR 图像，最终实现完全自动的色调映射。