论文标题
展现模糊的图像
Unfolding a blurred image
论文作者
论文摘要
我们提出了一种解决方案，目的是从单个运动中提取视频模糊图像，以在曝光期间按照摄像机看到的相机看到的场景清晰视图。我们首先通过培训卷积的反复视频自动编码器网络，以无监督的方式从敏锐的视频中学习运动表示形式，该网络执行视频重建的替代任务。经过培训后，它将用于对模糊图像的运动编码器进行指导培训。该网络从模糊的图像中提取嵌入式运动信息，以与受过训练的经过的视频解码器结合使用尖锐的视频。作为中级步骤，我们还设计了一个有效的体系结构，该体系结构能够实现实时单图像脱毛，并且在所有因素上都超越了竞争方法：准确性，速度和紧凑性。在真实场景和标准数据集上进行的实验证明了我们的框架优于最先进的框架及其生成一个合理的时间一致锋利框架序列的能力。
We present a solution for the goal of extracting a video from a single motion blurred image to sequentially reconstruct the clear views of a scene as beheld by the camera during the time of exposure. We first learn motion representation from sharp videos in an unsupervised manner through training of a convolutional recurrent video autoencoder network that performs a surrogate task of video reconstruction. Once trained, it is employed for guided training of a motion encoder for blurred images. This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder. As an intermediate step, we also design an efficient architecture that enables real-time single image deblurring and outperforms competing methods across all factors: accuracy, speed, and compactness. Experiments on real scenes and standard datasets demonstrate the superiority of our framework over the state-of-the-art and its ability to generate a plausible sequence of temporally consistent sharp frames.
