论文标题
通过显着区域检测进行散焦模糊检测
Defocus Blur Detection via Salient Region Detection Prior
论文作者
论文摘要
当人们通过数字单镜头反射摄像头（DSLR）拍摄照片时，depocus模糊总是发生在照片中，从而给人以显着的区域和美感。 Defocus Blur检测旨在将照片中的异常和深度区域分开，这是计算机视觉中的重要工作。当前用于散焦模糊检测的工作主要集中于网络设计，优化损失函数以及多流策略的应用，同时，这些作品并不关注培训数据的短缺。在这项工作中，为了解决上述数据差问题，我们转向重新考虑两个任务之间的关系：depocus Blur检测和显着区域检测。在具有散景效应的图像中，很明显，在大多数情况下，显着区域和田间区域重叠。因此，我们首先将网络训练在显着区域检测任务上，然后将预训练的模型转移到defocus模糊检测任务中。此外，我们提出了一个新颖的网络，用于散焦模糊检测。实验表明，我们的转移策略在许多当前模型上都很好，并证明了我们网络的优势。
Defocus blur always occurred in photos when people take photos by Digital Single Lens Reflex Camera(DSLR), giving salient region and aesthetic pleasure. Defocus blur Detection aims to separate the out-of-focus and depth-of-field areas in photos, which is an important work in computer vision. Current works for defocus blur detection mainly focus on the designing of networks, the optimizing of the loss function, and the application of multi-stream strategy, meanwhile, these works do not pay attention to the shortage of training data. In this work, to address the above data-shortage problem, we turn to rethink the relationship between two tasks: defocus blur detection and salient region detection. In an image with bokeh effect, it is obvious that the salient region and the depth-of-field area overlap in most cases. So we first train our network on the salient region detection tasks, then transfer the pre-trained model to the defocus blur detection tasks. Besides, we propose a novel network for defocus blur detection. Experiments show that our transfer strategy works well on many current models, and demonstrate the superiority of our network.
