  This paper considers the problem of generating an HDR image of a scene from
its LDR images. Recent studies employ deep learning and solve the problem in an
end-to-end fashion, leading to significant performance improvements. However,
it is still hard to generate a good quality image from LDR images of a dynamic
scene captured by a hand-held camera, e.g., occlusion due to the large motion
of foreground objects, causing ghosting artifacts. The key to success relies on
how well we can fuse the input images in their feature space, where we wish to
remove the factors leading to low-quality image generation while performing the
fundamental computations for HDR image generation, e.g., selecting the
best-exposed image/region. We propose a novel method that can better fuse the
features based on two ideas. One is multi-step feature fusion; our network
gradually fuses the features in a stack of blocks having the same structure.
The other is the design of the component block that effectively performs two
operations essential to the problem, i.e., comparing and selecting appropriate
images/regions. Experimental results show that the proposed method outperforms
the previous state-of-the-art methods on the standard benchmark tests.

Computer Vision and Pattern Recognition