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While today's high dynamic range (HDR) image fusion algorithms are capable ofblending multiple exposures, the acquisition is often controlled so that thedynamic range within one exposure is narrow. For HDR imaging in photon-limitedsituations, the dynamic range can be enormous and the noise within one exposureis spatially varying. Existing image denoising algorithms and HDR fusionalgorithms both fail to handle this situation, leading to severe limitations inlow-light HDR imaging. This paper presents two contributions. Firstly, weidentify the source of the problem. We find that the issue is associated withthe co-existence of (1) spatially varying signal-to-noise ratio, especially theexcessive noise due to very dark regions, and (2) a wide luminance range withineach exposure. We show that while the issue can be handled by a bank ofdenoisers, the complexity is high. Secondly, we propose a new method called thespatially varying high dynamic range (SV-HDR) fusion network to simultaneouslydenoise and fuse images. We introduce a new exposure-shared block within ourcustom-designed multi-scale transformer framework. In a variety of testingconditions, the performance of the proposed SV-HDR is better than the existingmethods.

HDR Imaging with Spatially Varying Signal-to-Noise Ratios