  While today's high dynamic range (HDR) image fusion algorithms are capable of
blending multiple exposures, the acquisition is often controlled so that the
dynamic range within one exposure is narrow. For HDR imaging in photon-limited
situations, the dynamic range can be enormous and the noise within one exposure
is spatially varying. Existing image denoising algorithms and HDR fusion
algorithms both fail to handle this situation, leading to severe limitations in
low-light HDR imaging. This paper presents two contributions. Firstly, we
identify the source of the problem. We find that the issue is associated with
the co-existence of (1) spatially varying signal-to-noise ratio, especially the
excessive noise due to very dark regions, and (2) a wide luminance range within
each exposure. We show that while the issue can be handled by a bank of
denoisers, the complexity is high. Secondly, we propose a new method called the
spatially varying high dynamic range (SV-HDR) fusion network to simultaneously
denoise and fuse images. We introduce a new exposure-shared block within our
custom-designed multi-scale transformer framework. In a variety of testing
conditions, the performance of the proposed SV-HDR is better than the existing
methods.
