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Tone mapping aims to convert high dynamic range (HDR) images to low dynamicrange (LDR) representations, a critical task in the camera imaging pipeline. Inrecent years, 3-Dimensional LookUp Table (3D LUT) based methods have gainedattention due to their ability to strike a favorable balance betweenenhancement performance and computational efficiency. However, these methodsoften fail to deliver satisfactory results in local areas since the look-uptable is a global operator for tone mapping, which works based on pixel valuesand fails to incorporate crucial local information. To this end, this paperaims to address this issue by exploring a novel strategy that integrates globaland local operators by utilizing closed-form Laplacian pyramid decompositionand reconstruction. Specifically, we employ image-adaptive 3D LUTs tomanipulate the tone in the low-frequency image by leveraging the specificcharacteristics of the frequency information. Furthermore, we utilize localLaplacian filters to refine the edge details in the high-frequency componentsin an adaptive manner. Local Laplacian filters are widely used to preserve edgedetails in photographs, but their conventional usage involves manual tuning andfixed implementation within camera imaging pipelines or photo editing tools. Wepropose to learn parameter value maps progressively for local Laplacian filtersfrom annotated data using a lightweight network. Our model achievessimultaneous global tone manipulation and local edge detail preservation in anend-to-end manner. Extensive experimental results on two benchmark datasetsdemonstrate that the proposed method performs favorably againststate-of-the-art methods.

Lookup Table meets Local Laplacian Filter: Pyramid Reconstruction Network for Tone Mapping