Recently, significant progress has been achieved in deep image matting. Most
of the classical image matting methods are time-consuming and require an ideal
trimap which is difficult to attain in practice. A high efficient image matting
method based on a weakly annotated mask is in demand for mobile applications.
In this paper, we propose a novel method based on Deep Learning and Guided
Filter, called Inductive Guided Filter, which can tackle the real-time general
image matting task on mobile devices. We design a lightweight hourglass network
to parameterize the original Guided Filter method that takes an image and a
weakly annotated mask as input. Further, the use of Gabor loss is proposed for
training networks for complicated textures in image matting. Moreover, we
create an image matting dataset MAT-2793 with a variety of foreground objects.
Experimental results demonstrate that our proposed method massively reduces
running time with robust accuracy.

本文提出了一种基于深度学习和引导滤波的高效图像抠图方法，称为归纳引导滤波，可在移动设备上实现实时的一般图像抠图任务，并在复杂纹理的图像抠图中使用 Gabor 损失进行网络训练。实验结果表明，我们的方法大大减少了运行时间并保证了良好的准确性。

感应式引导滤波器：移动设备上基于弱标记蒙版的实时深度图像抠图

Inductive Guided Filter: Real-time Deep Image Matting with Weakly  Annotated Masks on Mobile Devices

Recent advances in generative adversarial networks (GANs) have shown great
potentials in realistic image synthesis whereas most existing works address
synthesis realism in either appearance space or geometry space but few in both.
This paper presents an innovative Spatial Fusion GAN (SF-GAN) that combines a
geometry synthesizer and an appearance synthesizer to achieve synthesis realism
in both geometry and appearance spaces. The geometry synthesizer learns
contextual geometries of background images and transforms and places foreground
objects into the background images unanimously. The appearance synthesizer
adjusts the color, brightness and styles of the foreground objects and embeds
them into background images harmoniously, where a guided filter is introduced
for detail preserving. The two synthesizers are inter-connected as mutual
references which can be trained end-to-end without supervision. The SF-GAN has
been evaluated in two tasks: (1) realistic scene text image synthesis for
training better recognition models; (2) glass and hat wearing for realistic
matching glasses and hats with real portraits. Qualitative and quantitative
comparisons with the state-of-the-art demonstrate the superiority of the
proposed SF-GAN.

该论文提出了一种创新的空间融合 GAN（SF-GAN），它结合了几何合成器和外观合成器，以在几何和外观空间中实现综合的逼真性。两个综合器彼此相互连接，可以进行端到端训练，无需监督。SF-GAN 已在两个任务中进行了评估，定性和定量比较证明了该方法的优越性。

图像生成的空间融合生成对抗网络

Spatial Fusion GAN for Image Synthesis

The guided filter is a technique for edge-aware image filtering. Because of
its nice visual quality, fast speed, and ease of implementation, the guided
filter has witnessed various applications in real products, such as image
editing apps in phones and stereo reconstruction, and has been included in
official MATLAB and OpenCV. In this note, we remind that the guided filter can
be simply sped up from O(N) time to O(N/s^2) time for a subsampling ratio s. In
a variety of applications, this leads to a speedup of >10x with almost no
visible degradation. We hope this acceleration will improve performance of
current applications and further popularize this filter. Code is released.

本文介绍了一种对引导滤波器的简单加速方法，通过采用子采样技术将其时间复杂度从 O (N) 优化到 O (N/s^2)，使其在图像编辑等实际应用中获得了较高的速度和可用性。