  Tone mapping aims to convert high dynamic range (HDR) images to low dynamic
range (LDR) representations, a critical task in the camera imaging pipeline. In
recent years, 3-Dimensional LookUp Table (3D LUT) based methods have gained
attention due to their ability to strike a favorable balance between
enhancement performance and computational efficiency. However, these methods
often fail to deliver satisfactory results in local areas since the look-up
table is a global operator for tone mapping, which works based on pixel values
and fails to incorporate crucial local information. To this end, this paper
aims to address this issue by exploring a novel strategy that integrates global
and local operators by utilizing closed-form Laplacian pyramid decomposition
and reconstruction. Specifically, we employ image-adaptive 3D LUTs to
manipulate the tone in the low-frequency image by leveraging the specific
characteristics of the frequency information. Furthermore, we utilize local
Laplacian filters to refine the edge details in the high-frequency components
in an adaptive manner. Local Laplacian filters are widely used to preserve edge
details in photographs, but their conventional usage involves manual tuning and
fixed implementation within camera imaging pipelines or photo editing tools. We
propose to learn parameter value maps progressively for local Laplacian filters
from annotated data using a lightweight network. Our model achieves
simultaneous global tone manipulation and local edge detail preservation in an
end-to-end manner. Extensive experimental results on two benchmark datasets
demonstrate that the proposed method performs favorably against
state-of-the-art methods.
