论文标题
位置感知的单图像反射去除
Location-aware Single Image Reflection Removal
论文作者
论文摘要
本文提出了一种新颖的位置感知的深度学习单图像反射方法。我们的网络具有反射检测模块，以回归概率反射置信图，以多尺度的拉普拉斯特征作为输入。该概率图告诉区域是以反射为主或以传输为主的区域，它被用作网络的提示，以在预测反射层和传输层时控制特征流。我们将网络设计为一个经常性网络，以逐步完善每次迭代的反射删除结果。新颖的是，我们利用拉普拉斯内核参数强调强烈反射的边界。它对强烈反射检测是有益的，并且基本上提高了反射删除结果的质量。广泛的实验验证了所提出的方法的优越性能，而不是最先进的方法。我们的代码和预训练的模型可以在https://github.com/zdlarr/location-aware-sirr上找到。
This paper proposes a novel location-aware deep-learning-based single image reflection removal method. Our network has a reflection detection module to regress a probabilistic reflection confidence map, taking multi-scale Laplacian features as inputs. This probabilistic map tells if a region is reflection-dominated or transmission-dominated, and it is used as a cue for the network to control the feature flow when predicting the reflection and transmission layers. We design our network as a recurrent network to progressively refine reflection removal results at each iteration. The novelty is that we leverage Laplacian kernel parameters to emphasize the boundaries of strong reflections. It is beneficial to strong reflection detection and substantially improves the quality of reflection removal results. Extensive experiments verify the superior performance of the proposed method over state-of-the-art approaches. Our code and the pre-trained model can be found at https://github.com/zdlarr/Location-aware-SIRR.
