# Examining Autoexposure for Challenging Scenes

arXiv: https://arxiv.org/abs/2309.04542 · HTML: https://mlsanity.com/paper/2309.04542/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [HDR image reconstruction from a single exposure using deep CNNs](https://mlsanity.com/paper/1710.07480/) (2017)
- [Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline](https://mlsanity.com/paper/2004.01179/) (2020)
- [HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset](https://mlsanity.com/paper/2103.14943/) (2021)
- [GenISP: Neural ISP for Low-Light Machine Cognition](https://mlsanity.com/paper/2205.03688/) (2022)
- [Personalized Exposure Control Using Adaptive Metering and Reinforcement Learning](https://mlsanity.com/paper/1803.02269/) (2018)
- [Compression of High Dynamic Range Video Using the HEVC and H.264/AVC Standards](https://mlsanity.com/paper/1803.04823/) (2018)
- [Automatic Exposure Compensation for Multi-Exposure Image Fusion](https://mlsanity.com/paper/1805.11211/) (2018)

## Built on by (most cited first, 2 of 2)

- [Burst Image Super-Resolution with Base Frame Selection](https://mlsanity.com/paper/2406.17869/) (2024)
- [Neural Block-wise Auto-exposure for Single-Shot High Dynamic Range Imaging](https://mlsanity.com/paper/2610.18733/) (2026)

## Abstract (arXiv)

Autoexposure (AE) is a critical step applied by camera systems to ensure properly exposed images. While current AE algorithms are effective in well-lit environments with constant illumination, these algorithms still struggle in environments with bright light sources or scenes with abrupt changes in lighting. A significant hurdle in developing new AE algorithms for challenging environments, especially those with time-varying lighting, is the lack of suitable image datasets. To address this issue, we have captured a new 4D exposure dataset that provides a large solution space (i.e., shutter speed range from (1/500 to 15 seconds) over a temporal sequence with moving objects, bright lights, and varying lighting. In addition, we have designed a software platform to allow AE algorithms to be used in a plug-and-play manner with the dataset. Our dataset and associate platform enable repeatable evaluation of different AE algorithms and provide a much-needed starting point to develop better AE methods. We examine several existing AE strategies using our dataset and show that most users prefer a simple saliency method for challenging lighting conditions.