  Wide dynamic range (WDR) images contain more scene details and contrast when
compared to common images. However, it requires tone mapping to process the
pixel values in order to display properly. The details of WDR images can
diminish during the tone mapping process. In this work, we address the problem
by combining a novel reformulated Laplacian pyramid and deep learning. The
reformulated Laplacian pyramid always decompose a WDR image into two frequency
bands where the low-frequency band is global feature-oriented, and the
high-frequency band is local feature-oriented. The reformulation preserves the
local features in its original resolution and condenses the global features
into a low-resolution image. The generated frequency bands are reconstructed
and fine-tuned to output the final tone mapped image that can display on the
screen with minimum detail and contrast loss. The experimental results
demonstrate that the proposed method outperforms state-of-the-art WDR image
tone mapping methods. The code is made publicly available at
https://github.com/linmc86/Deep-Reformulated-Laplacian-Tone-Mapping.
