# Parameter Blending for Multi-Camera Harmonization for Automotive Surround View Systems

arXiv: https://arxiv.org/abs/2406.11066 · HTML: https://mlsanity.com/paper/2406.11066/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 16 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [High-Resolution Image Harmonization via Collaborative Dual Transformations](https://mlsanity.com/paper/2109.06671/) (2021)
- [Auto White-Balance Correction for Mixed-Illuminant Scenes](https://mlsanity.com/paper/2109.08750/) (2021)
- [Camera Calibration with Pose Guidance](https://mlsanity.com/paper/2102.10202/) (2021)
- [Novel Intensity Mapping Functions: Weighted Histogram Averaging](https://mlsanity.com/paper/2111.07283/) (2021)

## Abstract (arXiv)

In a surround view system, the image color and tone captured by multiple cameras can be different due to cameras applying auto white balance (AWB), global tone mapping (GTM) individually for each camera. The color and brightness along stitched seam location may look discontinuous among multiple cameras which impacts overall stitched image visual quality. To improve the color transition between adjacent cameras in stitching algorithm, we propose harmonization algorithm which applies before stitching to adjust multiple cameras' color and tone so that stitched image has smoother color and tone transition between adjacent cameras. Our proposed harmonization algorithm consists of AWB harmonization and GTM harmonization leveraging Image Signal Processor (ISP)'s AWB and GTM metadata statistics. Experiment result shows that our proposed algorithm outperforms global color transfer method in both visual quality and computational cost.