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.

翻译：色调映射旨在将高动态范围（HDR）图像转换为低动态范围（LDR）表示，这是相机成像管线中的关键任务。近年来，基于三维查找表（3D LUT）的方法因其在增强性能与计算效率之间取得良好平衡而受到关注。然而，由于查找表是一种基于像素值工作的全局色调映射算子，无法融合关键的局部信息，这些方法往往在局部区域难以获得令人满意的结果。为此，本文旨在通过探索一种利用闭式拉普拉斯金字塔分解与重建来整合全局和局部算子的新策略以解决该问题。具体而言，我们利用图像自适应3D LUT，通过频率信息的特定特征来操控低频图像的色调。此外，我们采用自适应局部拉普拉斯滤波器来优化高频分量中的边缘细节。局部拉普拉斯滤波器广泛用于保留照片中的边缘细节，但其传统使用方式涉及手动调参，并在相机成像管线或照片编辑工具中固定实现。我们提出使用轻量级网络从标注数据中渐进式学习局部拉普拉斯滤波器的参数值映射图。所提模型能以端到端方式实现全局色调操控与局部边缘细节保留。在两个基准数据集上的大量实验结果表明，该方法相比现有先进技术具有优越性能。