# Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping

arXiv: https://arxiv.org/abs/2109.00180 · HTML: https://mlsanity.com/paper/2109.00180/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Robust and interpretable blind image denoising via bias-free convolutional neural networks](https://mlsanity.com/paper/1906.05478/) (2019)
- [Fast Image Processing with Fully-Convolutional Networks](https://mlsanity.com/paper/1709.00643/) (2017)
- [Perceptually Optimized Image Rendering](https://mlsanity.com/paper/1701.06641/) (2017)

## Built on by (most cited first, 6 of 6)

- [Learning Diverse Tone Styles for Image Retouching](https://mlsanity.com/paper/2207.05430/) (2022)
- [Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning](https://mlsanity.com/paper/2303.07327/) (2023)
- [Video Quality Assessment for Online Processing: From Spatial to Temporal Sampling](https://mlsanity.com/paper/2501.07087/) (2025)
- [A Perceptually Optimized and Self-Calibrated Tone Mapping Operator](https://mlsanity.com/paper/2206.09146/) (2022)
- [Multitask Auxiliary Network for Perceptual Quality Assessment of Non-Uniformly Distorted Omnidirectional Images](https://mlsanity.com/paper/2501.11512/) (2025)
- [Computational Analysis of Degradation Modeling in Blind Panoramic Image Quality Assessment](https://mlsanity.com/paper/2503.03255/) (2025)

## Abstract (arXiv)

We describe a deep high-dynamic-range (HDR) image tone mapping operator that is computationally efficient and perceptually optimized. We first decompose an HDR image into a normalized Laplacian pyramid, and use two deep neural networks (DNNs) to estimate the Laplacian pyramid of the desired tone-mapped image from the normalized representation. We then end-to-end optimize the entire method over a database of HDR images by minimizing the normalized Laplacian pyramid distance (NLPD), a recently proposed perceptual metric. Qualitative and quantitative experiments demonstrate that our method produces images with better visual quality, and runs the fastest among existing local tone mapping algorithms.