Learning Differential Pyramid Representation for Tone Mapping

TL;DR

DPRNet is a novel learnable differential pyramid framework for high-fidelity tone mapping that adaptively captures high-frequency details and enforces perceptual consistency, outperforming existing methods on HDR datasets.

Contribution

Introduces DPRNet, a learnable differential pyramid that generalizes traditional pyramids for adaptive high-frequency detail capture in tone mapping.

Findings

Achieves state-of-the-art PSNR improvements on HDR datasets.

Produces perceptually coherent, detail-preserving tone-mapped images.

Effectively balances global tone perception and local contrast enhancement.

Abstract

Existing tone mapping methods operate on downsampled inputs and rely on handcrafted pyramids to recover high-frequency details. These designs typically fail to preserve fine textures and structural fidelity in complex HDR scenes. Furthermore, most methods lack an effective mechanism to jointly model global tone consistency and local contrast enhancement, leading to globally flat or locally inconsistent outputs such as halo artifacts. We present the Differential Pyramid Representation Network (DPRNet), an end-to-end framework for high-fidelity tone mapping. At its core is a learnable differential pyramid that generalizes traditional Laplacian and Difference-of-Gaussian pyramids through content-aware differencing operations across scales. This allows DPRNet to adaptively capture high-frequency variations under diverse luminance and contrast conditions. To enforce perceptual consistency, DPRNet incorporates global tone perception and local tone tuning modules operating on downsampled inputs, enabling efficient yet expressive tone adaptation. Finally, an iterative detail enhancement module progressively restores the full-resolution output in a coarse-to-fine manner, reinforcing structure and sharpness. Experiments show that DPRNet achieves state-of-the-art results, improving PSNR by 2.39 dB on the 4K HDR+ dataset and 3.01 dB on the 4K HDRI Haven dataset, while producing perceptually coherent, detail-preserving results. \textit{We provide an anonymous online demo at https://xxxxxxdprnet.github.io/DPRNet/.