DomainPlus: Cross Transform Domain Learning towards High Dynamic Range Imaging

High dynamic range (HDR) imaging by combining multiple low dynamic range (LDR) images of different exposures provides a promising way to produce high quality photographs. However, the misalignment between the input images leads to ghosting artifacts in the reconstructed HDR image. In this paper, we propose a cross-transform domain neural network for efficient HDR imaging. Our approach consists of two modules: a merging module and a restoration module. For the merging module, we propose a Multiscale Attention with Fronted Fusion (MAFF) mechanism to achieve coarse-to-fine spatial fusion. For the restoration module, we propose fronted Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT)-based learnable bandpass filters to formulate a cross-transform domain learning block, dubbed DomainPlus Block (DPB) for effective ghosting removal. Our ablation study and comprehensive experiments show that DomainPlus outperforms the existing state-of-the-art on several datasets.