# Neural Camera Simulators

arXiv: https://arxiv.org/abs/2104.05237 · HTML: https://mlsanity.com/paper/2104.05237/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 50 references that are in the map.
- First posted on arXiv 12 Apr 2021.

## Builds on (most cited first, 27 of 27)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [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)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [State of the Art on Neural Rendering](https://mlsanity.com/paper/2004.03805/) (2020)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [Single Image Portrait Relighting](https://mlsanity.com/paper/1905.00824/) (2019)
- [Neural Rerendering in the Wild](https://mlsanity.com/paper/1904.04290/) (2019)
- [Zoom To Learn, Learn To Zoom](https://mlsanity.com/paper/1905.05169/) (2019)
- [Noise Flow: Noise Modeling with Conditional Normalizing Flows](https://mlsanity.com/paper/1908.08453/) (2019)
- [Synthetic Depth-of-Field with a Single-Camera Mobile Phone](https://mlsanity.com/paper/1806.04171/) (2018)
- [Polarized Reflection Removal with Perfect Alignment in the Wild](https://mlsanity.com/paper/2003.12789/) (2020)
- [Towards Real Scene Super-Resolution with Raw Images](https://mlsanity.com/paper/1905.12156/) (2019)
- [Fast Image Processing with Fully-Convolutional Networks](https://mlsanity.com/paper/1709.00643/) (2017)
- [Robust Reflection Removal with Reflection-free Flash-only Cues](https://mlsanity.com/paper/2103.04273/) (2021)
- [A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimation](https://mlsanity.com/paper/1704.08992/) (2017)
- [Aperture Supervision for Monocular Depth Estimation](https://mlsanity.com/paper/1711.07933/) (2017)
- [Neural Network Generalization: The impact of camera parameters](https://mlsanity.com/paper/1912.03604/) (2019)
- [DeepLens: Shallow Depth Of Field From A Single Image](https://mlsanity.com/paper/1810.08100/) (2018)
- [Decouple Learning for Parameterized Image Operators](https://mlsanity.com/paper/1807.08186/) (2018)
- [Learning the image processing pipeline](https://mlsanity.com/paper/1605.09336/) (2016)
- [ISETAuto: Detecting vehicles with depth and radiance information](https://mlsanity.com/paper/2101.01843/) (2021)
- [Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks](https://mlsanity.com/paper/1612.01227/) (2016)
- [Learning to Detect Multiple Photographic Defects](https://mlsanity.com/paper/1612.01635/) (2016)

## Built on by (most cited first, 10 of 10)

- [Abandoning the Bayer-Filter to See in the Dark](https://mlsanity.com/paper/2203.04042/) (2022)
- [Blind Video Deflickering by Neural Filtering with a Flawed Atlas](https://mlsanity.com/paper/2303.08120/) (2023)
- [Generative Photography: Scene-Consistent Camera Control for Realistic Text-to-Image Synthesis](https://mlsanity.com/paper/2412.02168/) (2024)
- [A Categorized Reflection Removal Dataset with Diverse Real-world Scenes](https://mlsanity.com/paper/2108.03380/) (2021)
- [Real-Time Neural Character Rendering with Pose-Guided Multiplane Images](https://mlsanity.com/paper/2204.11820/) (2022)
- [Inverting the Imaging Process by Learning an Implicit Camera Model](https://mlsanity.com/paper/2304.12748/) (2023)
- [Exposure Correction Model to Enhance Image Quality](https://mlsanity.com/paper/2204.10648/) (2022)
- [RAWild: Sensor-Agnostic RAW Object Detection via Physics-Guided Curve and Grid Modeling](https://mlsanity.com/paper/2605.05941/) (2026)
- [DiffPhysCam: Differentiable Physics-Based Camera Simulation for Inverse Rendering and Embodied AI](https://mlsanity.com/paper/2508.08831/) (2025)
- [DeltaCam: Differential Intrinsic Camera Modeling for Video Generation](https://mlsanity.com/paper/2605.25266/) (2026)

## Abstract (arXiv)

We present a controllable camera simulator based on deep neural networks to synthesize raw image data under different camera settings, including exposure time, ISO, and aperture. The proposed simulator includes an exposure module that utilizes the principle of modern lens designs for correcting the luminance level. It also contains a noise module using the noise level function and an aperture module with adaptive attention to simulate the side effects on noise and defocus blur. To facilitate the learning of a simulator model, we collect a dataset of the 10,000 raw images of 450 scenes with different exposure settings. Quantitative experiments and qualitative comparisons show that our approach outperforms relevant baselines in raw data synthesize on multiple cameras. Furthermore, the camera simulator enables various applications, including large-aperture enhancement, HDR, auto exposure, and data augmentation for training local feature detectors. Our work represents the first attempt to simulate a camera sensor's behavior leveraging both the advantage of traditional raw sensor features and the power of data-driven deep learning.