论文标题
高效的单图像盲目脱毛的深层网络
Deep Idempotent Network for Efficient Single Image Blind Deblurring
论文作者
论文摘要
单图像盲目的脱毛物既不知道潜在的锋利图像也不是模糊的内核。尽管已经取得了很大的进展，但对于盲目的造成了一些主要的困难，包括高性能脱张和实时处理之间的权衡。此外，我们观察到，当前的单图像盲目脱毛网络无法进一步改善或稳定性能，但是当重复使用重新塑造时，可以显着降低性能。这意味着这些网络在建模理想的过度造成过程中的局限性。在这项工作中，我们做出了两项贡献来解决上述困难：（1）我们将同性恋的约束引入过度的框架中，并提出一个深层的势力网络，以实现改进的盲目的非均匀脱毛性能，并稳定地重新塑造。 （2）我们提出了一个简单而有效的Deblurring网络，具有轻巧的编码器编码单元和一个可以以渐进的残留方式脱布图像的经常性结构。关于合成和现实数据集的广泛实验证明了我们提出的框架的优越性。值得注意的是，我们提出的网络比最先进的网络近6.5倍，同时获得可比的高性能。
Single image blind deblurring is highly ill-posed as neither the latent sharp image nor the blur kernel is known. Even though considerable progress has been made, several major difficulties remain for blind deblurring, including the trade-off between high-performance deblurring and real-time processing. Besides, we observe that current single image blind deblurring networks cannot further improve or stabilize the performance but significantly degrades the performance when re-deblurring is repeatedly applied. This implies the limitation of these networks in modeling an ideal deblurring process. In this work, we make two contributions to tackle the above difficulties: (1) We introduce the idempotent constraint into the deblurring framework and present a deep idempotent network to achieve improved blind non-uniform deblurring performance with stable re-deblurring. (2) We propose a simple yet efficient deblurring network with lightweight encoder-decoder units and a recurrent structure that can deblur images in a progressive residual fashion. Extensive experiments on synthetic and realistic datasets prove the superiority of our proposed framework. Remarkably, our proposed network is nearly 6.5X smaller and 6.4X faster than the state-of-the-art while achieving comparable high performance.
