# Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image Fusion

arXiv: https://arxiv.org/abs/2404.06033 · HTML: https://mlsanity.com/paper/2404.06033/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Early Convolutions Help Transformers See Better](https://mlsanity.com/paper/2106.14881/) (2021)
- [Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement](https://mlsanity.com/paper/2012.05609/) (2020)
- [Benchmarking and Comparing Multi-exposure Image Fusion Algorithms](https://mlsanity.com/paper/2007.15156/) (2020)
- [A General Descent Aggregation Framework for Gradient-based Bi-level Optimization](https://mlsanity.com/paper/2102.07976/) (2021)
- [Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision](https://mlsanity.com/paper/2112.04719/) (2021)
- [Investigating Task-driven Latent Feasibility for Nonconvex Image Modeling](https://mlsanity.com/paper/1910.08242/) (2019)
- [Triple-level Model Inferred Collaborative Network Architecture for Video Deraining](https://mlsanity.com/paper/2111.04459/) (2021)

## Built on by (most cited first, 2 of 2)

- [FlexiD-Fuse: Flexible number of inputs multi-modal medical image fusion based on diffusion model](https://mlsanity.com/paper/2509.09456/) (2025)
- [Self-supervised Multiplex Consensus Mamba for General Image Fusion](https://mlsanity.com/paper/2512.20921/) (2025)

## Abstract (arXiv)

In recent years, deep learning networks have made remarkable strides in the domain of multi-exposure image fusion. Nonetheless, prevailing approaches often involve directly feeding over-exposed and under-exposed images into the network, which leads to the under-utilization of inherent information present in the source images. Additionally, unsupervised techniques predominantly employ rudimentary weighted summation for color channel processing, culminating in an overall desaturated final image tone. To partially mitigate these issues, this study proposes a gamma correction module specifically designed to fully leverage latent information embedded within source images. Furthermore, a modified transformer block, embracing with self-attention mechanisms, is introduced to optimize the fusion process. Ultimately, a novel color enhancement algorithm is presented to augment image saturation while preserving intricate details. The source code is available at https://github.com/ZhiyingDu/BHFMEF.