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.
翻译:虽然现今高动态范围(HDR)图像融合算法能够混合多个曝光,但是采集通常控制使得一个曝光内的动态范围较窄。在光子有限的低光HDR成像中,动态范围可能巨大,并且一个曝光内的噪声是空间变化的。现有图像去噪算法和HDR融合算法都无法处理这种情况,导致低光HDR成像的严重局限性。本文提出两个贡献。首先,我们确定了问题的根源。我们发现问题与(1)具有空间变化信噪比,特别是由于非常暗的区域而产生的过度噪声以及(2)每个曝光内的宽亮度范围的共存有关。我们证明了,虽然该问题可以通过一组去噪器来解决,但复杂度很高。其次,我们提出了一种名为空间变化高动态范围(SV-HDR)融合网络的新方法,以同时去噪和融合图像。我们在自定义多尺度变换器框架中引入了新的受曝光共享的块。在各种测试条件下,所提出的SV-HDR性能优于现有方法。