论文标题
用于真实图像修复的复合多分支特征融合
Compound Multi-branch Feature Fusion for Real Image Restoration
论文作者
论文摘要
图像恢复是一个具有挑战性且不足的问题，这也是一个长期存在的问题。但是，提出了大多数基于学习的恢复方法来靶向一种降解类型，这意味着它们缺乏泛化。在本文中，我们提出了一个由人类视觉系统（即视网膜神经节细胞）启发的多分支修复模型，该模型可以在一般框架中实现多个恢复任务。该实验表明，所提出的称为CMFNET的多个分支架构在四个数据集上具有竞争性能结果，包括图像Dehazing，DerainDrop和DeBlurring，这是自动驾驶汽车的非常常见的应用。可以在https://github.com/fanchimao/cmfnet上获得三个修复任务的源代码和预估计的模型。
Image restoration is a challenging and ill-posed problem which also has been a long-standing issue. However, most of learning based restoration methods are proposed to target one degradation type which means they are lack of generalization. In this paper, we proposed a multi-branch restoration model inspired from the Human Visual System (i.e., Retinal Ganglion Cells) which can achieve multiple restoration tasks in a general framework. The experiments show that the proposed multi-branch architecture, called CMFNet, has competitive performance results on four datasets, including image dehazing, deraindrop, and deblurring, which are very common applications for autonomous cars. The source code and pretrained models of three restoration tasks are available at https://github.com/FanChiMao/CMFNet.
