LAUSR

Abstract For High Dynamic Range (HDR) imaging systems, a new multi-exposure HDR imaging method based on Informative Content Enhanced Network (ICEN) is proposed to overcome the disadvantage that the existing deep learning based methods fails to fully exploit the differently exposed Low Dynamic Range (LDR) image contents to recover the details of the under/over-exposed regions. Specifically, the key contents in differently exposed LDR images that contribute to HDR imaging are firstly defined as Informative Contents (ICs). Then, the ICs are enhanced by the proposed Residual Channel Attention Module (RCAM), and then fused to generate HDR image. Furthermore, a generation scheme is designed for constructing HDR image labels and producing a multi-exposure HDR imaging dataset for training the proposed ICEN. The experimental results show that the proposed method is superior to the existing HDR imaging methods in quantitative and qualitative analysis, and can quickly generate high-quality HDR images.