# Learning the image processing pipeline

arXiv: https://arxiv.org/abs/1605.09336 · HTML: https://mlsanity.com/paper/1605.09336/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2016.

## Built on by (most cited first, 9 of 9)

- [You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction](https://mlsanity.com/paper/2205.14871/) (2022)
- [Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection](https://mlsanity.com/paper/2205.03346/) (2022)
- [Exploiting Raw Images for Real-Scene Super-Resolution](https://mlsanity.com/paper/2102.01579/) (2021)
- [Neural Camera Simulators](https://mlsanity.com/paper/2104.05237/) (2021)
- [ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline](https://mlsanity.com/paper/2212.05428/) (2022)
- [Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging](https://mlsanity.com/paper/2204.09871/) (2022)
- [Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancement](https://mlsanity.com/paper/2407.13170/) (2024)
- [ISETHDR: A Physics-based Synthetic Radiance Dataset for High Dynamic Range Driving Scenes](https://mlsanity.com/paper/2408.12048/) (2024)
- [Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline](https://mlsanity.com/paper/2206.01733/) (2022)

## Abstract (arXiv)

Many creative ideas are being proposed for image sensor designs, and these may be useful in applications ranging from consumer photography to computer vision. To understand and evaluate each new design, we must create a corresponding image processing pipeline that transforms the sensor data into a form that is appropriate for the application. The need to design and optimize these pipelines is time-consuming and costly. We explain a method that combines machine learning and image systems simulation that automates the pipeline design. The approach is based on a new way of thinking of the image processing pipeline as a large collection of local linear filters. We illustrate how the method has been used to design pipelines for novel sensor architectures in consumer photography applications.