# TGTM: TinyML-based Global Tone Mapping for HDR Sensors

arXiv: https://arxiv.org/abs/2405.05016 · HTML: https://mlsanity.com/paper/2405.05016/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 8 May 2024.

## Builds on (most cited first, 3 of 3)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement](https://mlsanity.com/paper/2204.13983/) (2022)
- [Very Lightweight Photo Retouching Network with Conditional Sequential Modulation](https://mlsanity.com/paper/2104.06279/) (2021)

## Built on by (most cited first, 1 of 1)

- [GC-ART: Global Learnable Second-Order Rational Tone Curves for Illumination Robustness](https://mlsanity.com/paper/2605.07329/) (2026)

## Abstract (arXiv)

Advanced driver assistance systems (ADAS) relying on multiple cameras are increasingly prevalent in vehicle technology. Yet, conventional imaging sensors struggle to capture clear images in conditions with intense illumination contrast, such as tunnel exits, due to their limited dynamic range. Introducing high dynamic range (HDR) sensors addresses this issue. However, the process of converting HDR content to a displayable range via tone mapping often leads to inefficient computations, when performed directly on pixel data. In this paper, we focus on HDR image tone mapping using a lightweight neural network applied on image histogram data. Our proposed TinyML-based global tone mapping method, termed as TGTM, operates at 9,000 FLOPS per RGB image of any resolution. Additionally, TGTM offers a generic approach that can be incorporated to any classical tone mapping method. Experimental results demonstrate that TGTM outperforms state-of-the-art methods on real HDR camera images by up to 5.85 dB higher PSNR with orders of magnitude less computations.