School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian 116620, China.
DUT-RU International School of Information Science & Engineering, Dalian University of Technology, Dalian 116620, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian 116620, China; Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China.
Neural Netw. 2018 May;101:101-112. doi: 10.1016/j.neunet.2018.01.012. Epub 2018 Feb 15.
Blind image deconvolution is one of the main low-level vision problems with wide applications. Many previous works manually design regularization to simultaneously estimate the latent sharp image and the blur kernel under maximum a posterior framework. However, it has been demonstrated that such joint estimation strategies may lead to the undesired trivial solution. In this paper, we present a novel perspective, using a stable feedback control system, to simulate the latent sharp image propagation. The controller of our system consists of regularization and guidance, which decide the sparsity and sharp features of latent image, respectively. Furthermore, the formational model of blind image is introduced into the feedback process to avoid the image restoration deviating from the stable point. The stability analysis of the system indicates the latent image propagation in blind deconvolution task can be efficiently estimated and controlled by cues and priors. Thus the kernel estimation used for image restoration becomes more precision. Experimental results show that our system is effective on image propagation, and can perform favorably against the state-of-the-art blind image deconvolution methods on different benchmark image sets and special blurred images.
盲图像反卷积是具有广泛应用的主要底层视觉问题之一。许多以前的工作都手动设计正则化项，以便在最大后验框架下同时估计潜在的清晰图像和模糊核。然而，已经证明，这种联合估计策略可能导致不理想的平凡解。在本文中，我们提出了一种新的视角，使用稳定的反馈控制系统来模拟潜在清晰图像的传播。我们系统的控制器由正则化项和引导项组成，分别决定潜在图像的稀疏性和清晰特征。此外，盲图像的形成模型被引入到反馈过程中，以避免图像恢复偏离稳定点。系统的稳定性分析表明，盲反卷积任务中的潜在图像传播可以通过线索和先验知识有效地估计和控制，从而使用于图像恢复的核估计更加精确。实验结果表明，我们的系统在图像传播方面是有效的，并且可以在不同的基准图像集和特殊模糊图像上对最新的盲图像反卷积方法进行很好的评估。
