# Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing

arXiv: https://arxiv.org/abs/1908.09191 · HTML: https://mlsanity.com/paper/1908.09191/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2019.

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

- [Optical Aberration Correction in Postprocessing using Imaging Simulation](https://mlsanity.com/paper/2305.05867/) (2023)
- [NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset](https://mlsanity.com/paper/2110.10364/) (2021)
- [RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Dataset](https://mlsanity.com/paper/2305.00767/) (2023)
- [Data Models for Dataset Drift Controls in Machine Learning With Optical Images](https://mlsanity.com/paper/2211.02578/) (2022)
- [CRISPnet: Color Rendition ISP Net](https://mlsanity.com/paper/2203.10562/) (2022)
- [LW-ISP: A Lightweight Model with ISP and Deep Learning](https://mlsanity.com/paper/2210.03904/) (2022)
- [ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing](https://mlsanity.com/paper/2305.11994/) (2023)
- [Leveraging Motion Estimation for Efficient Bayer-Domain Video Convolutional Networks](https://mlsanity.com/paper/2501.15119/) (2025)

## Abstract (arXiv)

A conventional camera performs various signal processing steps sequentially to reconstruct an image from a raw Bayer image. When performing these processing in multiple stages the residual error from each stage accumulates in the image and degrades the quality of the final reconstructed image. In this paper, we present a fully convolutional neural network (CNN) to perform defect pixel correction, denoising, white balancing, exposure correction, demosaicing, color transform, and gamma encoding. To our knowledge, this is the first CNN trained end-to-end to perform the entire image signal processing pipeline in a camera. The neural network was trained using a large image database of raw Bayer images. Through extensive experiments, we show that the proposed CNN based image signal processing system performs better than the conventional signal processing pipelines that perform the processing sequentially.