# Novel Intensity Mapping Functions: Weighted Histogram Averaging

arXiv: https://arxiv.org/abs/2111.07283 · HTML: https://mlsanity.com/paper/2111.07283/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 14 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [Single Image Brightening via Multi-Scale Exposure Fusion with Hybrid Learning](https://mlsanity.com/paper/2007.02042/) (2020)

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

- [ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation](https://mlsanity.com/paper/2304.03608/) (2023)
- [Neural Augmentation Based Panoramic High Dynamic Range Stitching](https://mlsanity.com/paper/2409.04679/) (2024)
- [Parameter Blending for Multi-Camera Harmonization for Automotive Surround View Systems](https://mlsanity.com/paper/2406.11066/) (2024)

## Abstract (arXiv)

It is challenging to align the brightness distribution of the images with different exposures due to possible color distortion and loss of details in the brightest and darkest regions of input images. In this paper, a novel intensity mapping algorithm is first proposed by introducing a new concept of weighted histogram averaging (WHA). The proposed WHA algorithm leverages the correspondence between the histogram bins of two images which are built up by using the non-decreasing property of the intensity mapping functions (IMFs). Extensive experiments indicate that the proposed WHA algorithm significantly surpasses the related state-of-the-art intensity mapping methods.