论文标题
低量表误差控制在低光图像和视频增强中使用肩variance缩
Long Scale Error Control in Low Light Image and Video Enhancement Using Equivariance
论文作者
论文摘要
在黑暗中获得的图像框架很特别。仅乘以常数不会恢复图像。射击噪声，量化效果和摄像机非线性意味着颜色和相对光水平的估计很差。当前方法使用真实的黑色图像对学习映射。这些很难捕获。最近的一篇论文表明，模拟数据对可产生恢复的真正改进，这可能是因为易于获得大量的模拟数据。在本文中，我们表明，尊重的均值 - 恢复的像素的颜色应该相同，但是图像是裁剪的 - 对恢复的最新情况产生了真正的改进。我们表明，可以使用量表选择机制来改善重建。最后，我们表明我们的方法也会改善视频恢复。我们的方法进行了定量和定性评估。
Image frames obtained in darkness are special. Just multiplying by a constant doesn't restore the image. Shot noise, quantization effects and camera non-linearities mean that colors and relative light levels are estimated poorly. Current methods learn a mapping using real dark-bright image pairs. These are very hard to capture. A recent paper has shown that simulated data pairs produce real improvements in restoration, likely because huge volumes of simulated data are easy to obtain. In this paper, we show that respecting equivariance -- the color of a restored pixel should be the same, however the image is cropped -- produces real improvements over the state of the art for restoration. We show that a scale selection mechanism can be used to improve reconstructions. Finally, we show that our approach produces improvements on video restoration as well. Our methods are evaluated both quantitatively and qualitatively.
