论文标题
SJ-HD^2R：选择性关节高动态范围和动态场景的DeNoing成像
SJ-HD^2R: Selective Joint High Dynamic Range and Denoising Imaging for Dynamic Scenes
论文作者
论文摘要
在高光中，重影工子，运动模糊和低忠诚度是来自多个低动态范围（LDR）图像的高动态范围（HDR）成像的主要挑战。这些问题来自使用中等暴露的图像作为先前方法中的参考框架。为了处理它们，我们建议使用暴露不足的图像作为避免这些问题的参考。但是，暴露不足图像的黑暗区域中的沉重噪音成为一个新问题。因此，我们提出了一个联合HDR和Denoising管道，其中包含两个子网络：（i）一个预贬值网络（prednnet）通过利用暴露先验来自适应地脱氧输入LDR； （ii）金字塔级联融合网络（PCFNET），以多尺度的方式引入了注意机制和级联结构。为了进一步利用这两个范式，我们提出了一个选择性和联合HDR和DeNoing（SJ-HD $^2 $ R）成像框架，利用特定方案的先验来进行路径选择，准确的准确性超过93.3 $ \％$。我们创建了第一个关节HDR和Denoising基准数据集，该数据集包含各种具有挑战性的HDR和DeNoing场景，并支持参考图像的切换。广泛的实验结果表明，我们的方法可实现优于以前的方法。
Ghosting artifacts, motion blur, and low fidelity in highlight are the main challenges in High Dynamic Range (HDR) imaging from multiple Low Dynamic Range (LDR) images. These issues come from using the medium-exposed image as the reference frame in previous methods. To deal with them, we propose to use the under-exposed image as the reference to avoid these issues. However, the heavy noise in dark regions of the under-exposed image becomes a new problem. Therefore, we propose a joint HDR and denoising pipeline, containing two sub-networks: (i) a pre-denoising network (PreDNNet) to adaptively denoise input LDRs by exploiting exposure priors; (ii) a pyramid cascading fusion network (PCFNet), introducing an attention mechanism and cascading structure in a multi-scale manner. To further leverage these two paradigms, we propose a selective and joint HDR and denoising (SJ-HD$^2$R) imaging framework, utilizing scenario-specific priors to conduct the path selection with an accuracy of more than 93.3$\%$. We create the first joint HDR and denoising benchmark dataset, which contains a variety of challenging HDR and denoising scenes and supports the switching of the reference image. Extensive experiment results show that our method achieves superior performance to previous methods.
