# Joint Multi-Scale Tone Mapping and Denoising for HDR Image Enhancement

arXiv: https://arxiv.org/abs/2303.09071 · HTML: https://mlsanity.com/paper/2303.09071/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 60 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 23 of 23)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)
- [Toward Convolutional Blind Denoising of Real Photographs](https://mlsanity.com/paper/1807.04686/) (2018)
- [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks](https://mlsanity.com/paper/1506.05751/) (2015)
- [Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation](https://mlsanity.com/paper/2103.00860/) (2021)
- [Conditional Sequential Modulation for Efficient Global Image Retouching](https://mlsanity.com/paper/2009.10390/) (2020)
- [Deep Burst Denoising](https://mlsanity.com/paper/1712.05790/) (2017)
- [Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks](https://mlsanity.com/paper/1803.05215/) (2018)
- [Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement](https://mlsanity.com/paper/1911.11323/) (2019)
- [Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping](https://mlsanity.com/paper/2007.09187/) (2020)
- [Fast Enhancement for Non-Uniform Illumination Images using Light-weight CNNs](https://mlsanity.com/paper/2006.00439/) (2020)
- [Deep Learning for Image Denoising: A Survey](https://mlsanity.com/paper/1810.05052/) (2018)
- [An Analysis and Implementation of the HDR+ Burst Denoising Method](https://mlsanity.com/paper/2110.09354/) (2021)
- [Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw Images](https://mlsanity.com/paper/1905.05092/) (2019)
- [Identity Enhanced Residual Image Denoising](https://mlsanity.com/paper/2004.13523/) (2020)

## Built on by (most cited first, 7 of 7)

- [UltraFusion: Ultra High Dynamic Imaging using Exposure Fusion](https://mlsanity.com/paper/2501.11515/) (2025)
- [Enhancing RAW-to-sRGB with Decoupled Style Structure in Fourier Domain](https://mlsanity.com/paper/2401.02161/) (2024)
- [Learning Adaptive Lighting via Channel-Aware Guidance](https://mlsanity.com/paper/2412.01493/) (2024)
- [Learning Differential Pyramid Representation for Tone Mapping](https://mlsanity.com/paper/2412.01463/) (2024)
- [Joint tone mapping and denoising of thermal infrared images via multi-scale Retinex and multi-task learning](https://mlsanity.com/paper/2305.00691/) (2023)
- [From Darkness to Detail: Frequency-Aware SSMs for Low-Light Vision](https://mlsanity.com/paper/2408.09650/) (2024)
- [A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement](https://mlsanity.com/paper/2506.18323/) (2025)

## Abstract (arXiv)

An image processing unit (IPU), or image signal processor (ISP) for high dynamic range (HDR) imaging usually consists of demosaicing, white balancing, lens shading correction, color correction, denoising, and tone-mapping. Besides noise from the imaging sensors, almost every step in the ISP introduces or amplifies noise in different ways, and denoising operators are designed to reduce the noise from these sources. Designed for dynamic range compressing, tone-mapping operators in an ISP can significantly amplify the noise level, especially for images captured in low-light conditions, making denoising very difficult. Therefore, we propose a joint multi-scale denoising and tone-mapping framework that is designed with both operations in mind for HDR images. Our joint network is trained in an end-to-end format that optimizes both operators together, to prevent the tone-mapping operator from overwhelming the denoising operator. Our model outperforms existing HDR denoising and tone-mapping operators both quantitatively and qualitatively on most of our benchmarking datasets.