  Multi-exposure High Dynamic Range (HDR) imaging is a challenging task when
facing truncated texture and complex motion. Existing deep learning-based
methods have achieved great success by either following the alignment and
fusion pipeline or utilizing attention mechanism. However, the large
computation cost and inference delay hinder them from deploying on resource
limited devices. In this paper, to achieve better efficiency, a novel Selective
Alignment Fusion Network (SAFNet) for HDR imaging is proposed. After extracting
pyramid features, it jointly refines valuable area masks and cross-exposure
motion in selected regions with shared decoders, and then fuses high quality
HDR image in an explicit way. This approach can focus the model on finding
valuable regions while estimating their easily detectable and meaningful
motion. For further detail enhancement, a lightweight refine module is
introduced which enjoys privileges from previous optical flow, selection masks
and initial prediction. Moreover, to facilitate learning on samples with large
motion, a new window partition cropping method is presented during training.
Experiments on public and newly developed challenging datasets show that
proposed SAFNet not only exceeds previous SOTA competitors quantitatively and
qualitatively, but also runs order of magnitude faster. Code and dataset is
available at https://github.com/ltkong218/SAFNet.
