Bayer patterned high dynamic range image reconstruction using adaptive weighting function
Bayer patterned high dynamic range image reconstruction using adaptive weighting function
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DOI：
10.1186/1687-6180-2014-76
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发表时间：
2014-05-22
影响因子：
1.9
通讯作者：
Kang, Moon Gi
中科院分区：
文献类型：
--
作者：
Kang, Hee;Lee, Suk Ho;Kang, Moon Gi
由于大多数图像传感器的有限动态范围，直接从相机获取期望的高动态范围（HDR）图像并不容易。因此，通常使用称为HDR图像重建的后处理，其从一组不同曝光的图像重建HDR图像以克服有限的动态范围。然而，传统的HDR图像重建方法受到噪声因素和重影伪影的影响。这是由于以下事实：以短曝光时间拍摄的输入图像在暗区域中包含很多噪声，这导致重构HDR图像的对应暗区域中的噪声增加。此外，由于在不同时间获取输入图像，因此图像包含不同的运动信息，这导致重影伪影。在本文中，我们提出了一种HDR图像重建方法，减少噪声因素的影响，防止鬼伪影。为了减少噪声因素的影响，加权函数，它确定了一定的输入图像的重建HDR图像的贡献，设计适应曝光时间和局部运动。此外，加权函数的设计，以排除重影区域，通过考虑的亮度和色度值之间的差异，几个输入图像。与传统的方法，一般工作在彩色图像处理模块（IPM）处理，所提出的方法直接对拜耳原始图像。这允许线性相机响应函数，并且还提高了硬件实现的效率。实验结果表明，该方法可以重建高质量的Bayer模式HDR图像，同时对鬼伪影和噪声因素具有鲁棒性。
It is not easy to acquire a desired high dynamic range (HDR) image directly from a camera due to the limited dynamic range of most image sensors. Therefore, generally, a post-process called HDR image reconstruction is used, which reconstructs an HDR image from a set of differently exposed images to overcome the limited dynamic range. However, conventional HDR image reconstruction methods suffer from noise factors and ghost artifacts. This is due to the fact that the input images taken with a short exposure time contain much noise in the dark regions, which contributes to increased noise in the corresponding dark regions of the reconstructed HDR image. Furthermore, since input images are acquired at different times, the images contain different motion information, which results in ghost artifacts. In this paper, we propose an HDR image reconstruction method which reduces the impact of the noise factors and prevents ghost artifacts. To reduce the influence of the noise factors, the weighting function, which determines the contribution of a certain input image to the reconstructed HDR image, is designed to adapt to the exposure time and local motions. Furthermore, the weighting function is designed to exclude ghosting regions by considering the differences of the luminance and the chrominance values between several input images. Unlike conventional methods, which generally work on a color image processed by the image processing module (IPM), the proposed method works directly on the Bayer raw image. This allows for a linear camera response function and also improves the efficiency in hardware implementation. Experimental results show that the proposed method can reconstruct high-quality Bayer patterned HDR images while being robust against ghost artifacts and noise factors.