# Gradient-based Feature Extraction From Raw Bayer Pattern Images

arXiv: https://arxiv.org/abs/2004.02429 · HTML: https://mlsanity.com/paper/2004.02429/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Unsupervised Spectral Demosaicing with Lightweight Spectral Attention Networks](https://mlsanity.com/paper/2307.01990/) (2023)
- [Raw Bayer Pattern Image Synthesis for Computer Vision-oriented Image Signal Processing Pipeline Design](https://mlsanity.com/paper/2110.12823/) (2021)
- [R3: Reconstruction, Raw, and Rain: Deraining Directly in the Bayer Domain](https://mlsanity.com/paper/2509.24022/) (2025)

## Abstract (arXiv)

In this paper, the impact of demosaicing on gradient extraction is studied and a gradient-based feature extraction pipeline based on raw Bayer pattern images is proposed. It is shown both theoretically and experimentally that the Bayer pattern images are applicable to the central difference gradient-based feature extraction algorithms without performance degradation, or even with superior performance in some cases. The color difference constancy assumption, which is widely used in various demosaicing algorithms, is applied in the proposed Bayer pattern image-based gradient extraction pipeline. Experimental results show that the gradients extracted from Bayer pattern images are robust enough to be used in histogram of oriented gradients (HOG)-based pedestrian detection algorithms and shift-invariant feature transform (SIFT)-based matching algorithms.