论文标题
深度了解散景图的平滑图像的混合
Depth-aware Blending of Smoothed Images for Bokeh Effect Generation
论文作者
论文摘要
摄影中使用了散景效应，以捕获近距离物体看起来锋利的图像，而其他任何东西都保持异常。通常使用单镜头反射摄像头捕获散景照片。大多数现代智能手机都可以利用双后置摄像头或良好的自动对焦硬件来拍摄散景图像。但是，对于带有单重摄像机的智能手机，没有良好的自动对焦硬件，我们必须依靠软件来生成散景图像。这种系统也可用于在已经捕获的图像中生成散景效应。在本文中，提出了一个端到端的深度学习框架，以从图像中产生高质量的散景效应。将原始图像和不同版本的平滑图像混合在一起，以借助单眼深度估计网络产生散景效果。将提出的方法与基于显着性检测的基线和AIM 2019对散景效应综合挑战中提出的许多方法进行了比较。显示了广泛的实验，以了解所提出算法的不同部分。该网络轻巧，可以在0.03秒内处理高清图像。这种方法在AIM 2019玻璃效果挑战赛曲目中排名第二。
Bokeh effect is used in photography to capture images where the closer objects look sharp and every-thing else stays out-of-focus. Bokeh photos are generally captured using Single Lens Reflex cameras using shallow depth-of-field. Most of the modern smartphones can take bokeh images by leveraging dual rear cameras or a good auto-focus hardware. However, for smartphones with single-rear camera without a good auto-focus hardware, we have to rely on software to generate bokeh images. This kind of system is also useful to generate bokeh effect in already captured images. In this paper, an end-to-end deep learning framework is proposed to generate high-quality bokeh effect from images. The original image and different versions of smoothed images are blended to generate Bokeh effect with the help of a monocular depth estimation network. The proposed approach is compared against a saliency detection based baseline and a number of approaches proposed in AIM 2019 Challenge on Bokeh Effect Synthesis. Extensive experiments are shown in order to understand different parts of the proposed algorithm. The network is lightweight and can process an HD image in 0.03 seconds. This approach ranked second in AIM 2019 Bokeh effect challenge-Perceptual Track.
