Abstract: Existing multi-exposure image fusion methods in the sRGB domain are prone to generating ghost artifacts when tackling scenes with large motion amplitude; the fused high dynamic range (HDR) images have poor quality, and the algorithm efficiency is low. To this end, this paper proposed a multi-exposure image fusion model based on multi-scale alignment and fusion to directly perform multi-exposure image fusion in the RAW domain. The model designed a multi-scale deformable alignment module (MS-DAM) to implicitly align features of different exposures at multiple scales, thus achieving more accurate alignment and greatly reducing ghost artifacts. Meanwhile, to better perform multi-exposure feature fusion, this paper designed a progressive fusion module (PFM) to perform multiexposure feature fusion at multiple scales and predict the HDR images at the corresponding scales, with features beneficial to subsequent multi-exposure fusion gradually enhanced. Considering the lack of multi-exposure fusion datasets in the RAW domain, this paper constructed a benchmark dataset for multi-exposure image fusion based on the RAW domain. Experimental results show that the proposed method outperforms previous methods for multi-exposure image fusion based on the RAW domain.

Key words: multi-exposure image fusion, high dynamic range imaging, deformable convolution