High Dynamic Range Imaging via Visual Attention Modules

TL;DR

This paper introduces a deep learning HDR imaging model that uses visual attention modules to focus on informative image regions, outperforming existing methods in reconstructing high-quality HDR images.

Contribution

The novel model incorporates visual attention modules based on segmentation to enhance HDR image reconstruction, addressing limitations of prior methods.

Findings

Outperforms most state-of-the-art HDR algorithms

Utilizes visual attention to focus on informative image regions

Demonstrates improved HDR reconstruction quality

Abstract

Thanks to High Dynamic Range (HDR) imaging methods, the scope of photography has seen profound changes recently. To be more specific, such methods try to reconstruct the lost luminosity of the real world caused by the limitation of regular cameras from the Low Dynamic Range (LDR) images. Additionally, although the State-Of-The-Art methods in this topic perform well, they mainly concentrate on combining different exposures and have less attention to extracting the informative parts of the images. Thus, this paper aims to introduce a new model capable of incorporating information from the most visible areas of each image extracted by a visual attention module (VAM), which is a result of a segmentation strategy. In particular, the model, based on a deep learning architecture, utilizes the extracted areas to produce the final HDR image. The results demonstrate that our method outperformed most of the State-Of-The-Art algorithms.