Deep convolutional neural networks (DCNN) aided high dynamic range (HDR) imaging recently received a lot of attention. The quality of DCNN generated HDR images have overperformed the traditional counterparts. However, DCNN is prone to be computationally intensive and power-hungry. To address the challenge, we propose LightFuse, a light-weight CNN-based algorithm for extreme dual-exposure image fusion, which can be implemented on various embedded computing platforms with limited power and hardware resources. 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 display that the proposed technique could generate HDR images with plausible details in extremely exposed regions. Our PSNR score exceeds the other state-of-the-art approaches by 1.2 to 1.6 times and achieves 1.4 to 20 times FLOP and parameter reduction compared with others.
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