论文标题
SVBR-NET：非盲型在空间变化的散焦拆卸网络
SVBR-NET: A Non-Blind Spatially Varying Defocus Blur Removal Network
论文作者
论文摘要
Defocus Blur是大多数相机中使用的光学传感器的物理后果。尽管它可以用作摄影风格，但通常被视为图像降级，以形式为具有空间变化的模糊内核的尖锐图像的卷积。在过去几年的模糊估计方法的推动下，我们提出了一种非盲方法来处理图像脱毛的方法，可以处理空间变化的核。我们介绍了两个编码器子网络，它们分别用模糊图像和估计的模糊图馈入，并作为输出deblurred（Deconvolved）图像作为输出。每个子网络都会呈现几个跳过连接，这些连接允许分开分开的数据传播，还允许使用间隔的跳过连接，以简化模块之间的通信。该网络经过合成的模糊内核进行训练，这些核被增强以模仿现有的模糊估计方法产生的模糊图，我们的实验结果表明，当与多种模糊估计方法结合使用时，我们的方法很好地工作。
Defocus blur is a physical consequence of the optical sensors used in most cameras. Although it can be used as a photographic style, it is commonly viewed as an image degradation modeled as the convolution of a sharp image with a spatially-varying blur kernel. Motivated by the advance of blur estimation methods in the past years, we propose a non-blind approach for image deblurring that can deal with spatially-varying kernels. We introduce two encoder-decoder sub-networks that are fed with the blurry image and the estimated blur map, respectively, and produce as output the deblurred (deconvolved) image. Each sub-network presents several skip connections that allow data propagation from layers spread apart, and also inter-subnetwork skip connections that ease the communication between the modules. The network is trained with synthetically blur kernels that are augmented to emulate blur maps produced by existing blur estimation methods, and our experimental results show that our method works well when combined with a variety of blur estimation methods.
