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.
翻译:幽灵文物、运动模糊和亮点中的低忠诚度是来自多个低动态区域图像的高动态区域成像(HDR)的主要挑战。这些问题来自使用中度曝光图像作为先前方法的参考框架。为了解决这些问题,我们提议使用曝光不足的图像作为避免这些问题的参考。然而,曝光不足的图像的黑暗区域的强烈噪音成为一个新问题。因此,我们提议建立一个共同的《人类发展报告》和去音管道,其中包括两个子网络:(一) 预先隐蔽网络(PreDNNet),通过利用暴露前期的利用,适应性地隐蔽输入LDRS;(二) 金字形连锁网络(PCFNet),以多尺度引入关注机制和嵌入结构来避免这些问题。为进一步利用这两种模式,我们提议有选择和联合的《人类发展报告》和《去音管》成像框架(SJ-HD$2R),其中含有两个子网络,在选择路径之前,利用比93.3美元更精确的参考率,并支持了我们以往的《人类发展报告》和新版的模型的精确度。我们创建了一种具有挑战性的数据。