# Modelling the Scene Dependent Imaging in Cameras with a Deep Neural Network

arXiv: https://arxiv.org/abs/1707.08350 · HTML: https://mlsanity.com/paper/1707.08350/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2017.

## Built on by (most cited first, 8 of 8)

- [Learning sRGB-to-Raw-RGB De-rendering with Content-Aware Metadata](https://mlsanity.com/paper/2206.01813/) (2022)
- [RAW-Adapter: Adapting Pre-trained Visual Model to Camera RAW Images](https://mlsanity.com/paper/2408.14802/) (2024)
- [NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution](https://mlsanity.com/paper/2506.02197/) (2025)
- [GamutMLP: A Lightweight MLP for Color Loss Recovery](https://mlsanity.com/paper/2304.11743/) (2023)
- [RAW-Adapter: Adapting Pre-trained Visual Model to Camera RAW Images and A Benchmark](https://mlsanity.com/paper/2503.17027/) (2025)
- [Deep Metric Color Embeddings for Splicing Localization in Severely Degraded Images](https://mlsanity.com/paper/2206.10737/) (2022)
- [Edit-aware RAW Reconstruction](https://mlsanity.com/paper/2512.05859/) (2025)
- [RawGen: Learning Camera Raw Image Generation](https://mlsanity.com/paper/2604.00093/) (2026)

## Abstract (arXiv)

We present a novel deep learning framework that models the scene dependent image processing inside cameras. Often called as the radiometric calibration, the process of recovering RAW images from processed images (JPEG format in the sRGB color space) is essential for many computer vision tasks that rely on physically accurate radiance values. All previous works rely on the deterministic imaging model where the color transformation stays the same regardless of the scene and thus they can only be applied for images taken under the manual mode. In this paper, we propose a data-driven approach to learn the scene dependent and locally varying image processing inside cameras under the automode. Our method incorporates both the global and the local scene context into pixel-wise features via multi-scale pyramid of learnable histogram layers. The results show that we can model the imaging pipeline of different cameras that operate under the automode accurately in both directions (from RAW to sRGB, from sRGB to RAW) and we show how we can apply our method to improve the performance of image deblurring.