HDR-AGAN: GHOST-FREE HIGH DYNAMIC RANGE IMAGING WITH ATTENTION GUIDED ADVERSARIAL NETWORK

Creating a high dynamic range (HDR) image from a set of low dynamic range (LDR) images with multiple exposures is always a challenging task when there is motion among the LDR image stacks. In this paper, we propose a generative adversarial network-based method with an attention module, called HDR- AGAN, to deal with the issue caused by object motions. Given three dynamic LDR images with different exposures, the well-exposed one is treated as the reference image, while the other two images are aligned to the reference one so that two additional inputs are obtained in the proposed system. An attention module operated between the reference image and the non-reference image is employed for extracting useful features. The generated HDR image is judged by the discriminator which operates on specified image areas derived by the ghost detection module. Experimental results show that, compared to existing algorithms, our method generates HDR images with less ghost artifacts and also yields better image quality in terms of several objective metrics.