  Recently, high dynamic range (HDR) image reconstruction based on the multiple
exposure stack from a given single exposure utilizes a deep learning framework
to generate high-quality HDR images. These conventional networks focus on the
exposure transfer task to reconstruct the multi-exposure stack. Therefore, they
often fail to fuse the multi-exposure stack into a perceptually pleasant HDR
image as the inversion artifacts occur. We tackle the problem in stack
reconstruction-based methods by proposing a novel framework with a fully
differentiable high dynamic range imaging (HDRI) process. By explicitly using
the loss, which compares the network's output with the ground truth HDR image,
our framework enables a neural network that generates the multiple exposure
stack for HDRI to train stably. In other words, our differentiable HDR
synthesis layer helps the deep neural network to train to create multi-exposure
stacks while reflecting the precise correlations between multi-exposure images
in the HDRI process. In addition, our network uses the image decomposition and
the recursive process to facilitate the exposure transfer task and to
adaptively respond to recursion frequency. The experimental results show that
the proposed network outperforms the state-of-the-art quantitative and
qualitative results in terms of both the exposure transfer tasks and the whole
HDRI process.
