  Deep convolutional neural networks (DCNNs) have aided high dynamic range
(HDR) imaging recently and have received a lot of attention. The quality of
DCNN-generated HDR images has overperformed the traditional counterparts.
However, DCNNs are prone to be computationally intensive and power-hungry, and
hence cannot be implemented on various embedded computing platforms with
limited power and hardware resources. Embedded systems have a huge market, and
utilizing DCNNs' powerful functionality into them will further reduce human
intervention. To address the challenge, we propose LightFuse, a lightweight
CNN-based algorithm for extreme dual-exposure image fusion, which achieves
better functionality than a conventional DCNN and can be deployed in embedded
systems. Two sub-networks are utilized: a GlobalNet (G) and a DetailNet (D).
The goal of G is to learn the global illumination information on the spatial
dimension, whereas D aims to enhance local details on the channel dimension.
Both G and D are based solely on depthwise convolution (D_Conv) and pointwise
convolution (P_Conv) to reduce required parameters and computations.
Experimental results show that this proposed technique could generate HDR
images in extremely exposed regions with sufficient details to be legible. Our
model outperforms other state-of-the-art approaches in peak signal-to-noise
ratio (PSNR) score by 0.9 to 8.7 while achieving 16.7 to 306.2 times parameter
reduction.
