HDR Imaging with Spatially Varying Signal-to-Noise Ratios
This addresses limitations in low-light HDR imaging for applications like photography or scientific imaging, but it appears incremental as it builds on existing fusion and denoising techniques.
The paper tackles the problem of high dynamic range (HDR) imaging in photon-limited situations, where existing methods fail due to spatially varying noise and wide luminance ranges, and proposes a new method called SV-HDR that outperforms existing approaches in various testing conditions.
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.