# Neural Augmentation Based Panoramic High Dynamic Range Stitching

arXiv: https://arxiv.org/abs/2409.04679 · HTML: https://mlsanity.com/paper/2409.04679/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 7 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [CycleISP: Real Image Restoration via Improved Data Synthesis](https://mlsanity.com/paper/2003.07761/) (2020)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation](https://mlsanity.com/paper/2304.03608/) (2023)
- [ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction](https://mlsanity.com/paper/2112.02906/) (2021)
- [Towards High-quality HDR Deghosting with Conditional Diffusion Models](https://mlsanity.com/paper/2311.00932/) (2023)
- [Parallax-Tolerant Unsupervised Deep Image Stitching](https://mlsanity.com/paper/2302.08207/) (2023)
- [Single-Perspective Warps in Natural Image Stitching](https://mlsanity.com/paper/1802.04645/) (2018)
- [Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot](https://mlsanity.com/paper/2111.07281/) (2021)
- [Novel Intensity Mapping Functions: Weighted Histogram Averaging](https://mlsanity.com/paper/2111.07283/) (2021)
- [Single-Image HDR Reconstruction Assisted Ghost Suppression and Detail Preservation Network for Multi-Exposure HDR Imaging](https://mlsanity.com/paper/2403.04228/) (2024)

## Built on by (most cited first, 1 of 1)

- [Multi-exposure HDR Imaging: A Review of Pixel-level and Feature-level Reconstruction Methods](https://mlsanity.com/paper/2608.28674/) (2026)

## Abstract (arXiv)

Due to saturated regions of inputting low dynamic range (LDR) images and large intensity changes among the LDR images caused by different exposures, it is challenging to produce an information enriched panoramic LDR image without visual artifacts for a high dynamic range (HDR) scene through stitching multiple geometrically synchronized LDR images with different exposures and pairwise overlapping fields of views (OFOVs). Fortunately, the stitching of such images is innately a perfect scenario for the fusion of a physics-driven approach and a data-driven approach due to their OFOVs. Based on this new insight, a novel neural augmentation based panoramic HDR stitching algorithm is proposed in this paper. The physics-driven approach is built up using the OFOVs. Different exposed images of each view are initially generated by using the physics-driven approach, are then refined by a data-driven approach, and are finally used to produce panoramic LDR images with different exposures. All the panoramic LDR images with different exposures are combined together via a multi-scale exposure fusion algorithm to produce the final panoramic LDR image. Experimental results demonstrate the proposed algorithm outperforms existing panoramic stitching algorithms.