Total variation based image deconvolution for extended depth-of-field microscopy images 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. Thus the kernel estimation used for image restoration becomes more precision. 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. Furthermore, the formational model of blind image is introduced into the feedback process to avoid the image restoration deviating from the stable point. The controller of our system consists of regularization and guidance, which decide the sparsity and sharp features of latent image, respectively. In this paper, we present a novel perspective, using a stable feedback control system, to simulate the latent sharp image propagation. However, it has been demonstrated that such joint estimation strategies may lead to the undesired trivial solution. Many previous works manually design regularization to simultaneously estimate the latent sharp image and the blur kernel under maximum a posterior framework. Numerical examples for blind image restoration are given to show that the proposed method outperforms some existing methods in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), visual quality and time consumption.ĭesigning a stable feedback control system for blind image deconvolution.Ĭheng, Shichao Liu, Risheng Fan, Xin Luo, Zhongxuanīlind image deconvolution is one of the main low-level vision problems with wide applications. We employ the first-order primal-dual method for the solution of the proposed minimization model. The proposed minimization model is shown to be convex. Meanwhile, we use the total variation to regularize the resulting image obtained by convolving the inverse filter with the degraded image. Inspired by the oscillation structure of the inverse filters, we propose to use the star norm to regularize the inverse filter. By studying the inverse filters of signal and image restoration problems, we observe the oscillation structure of the inverse filters. We assume that the support region of the blur object is known, as has been done in a few existing works. In this paper, we investigate a convex regularized inverse filtering method for blind deconvolution of images. Regularization methods are used to handle the ill-posedness of blind deconvolution and get meaningful solutions. It is a bilinear ill-posed inverse problem corresponding to the direct problem of convolution. Both synthetic and real-world examples show that more accurate PSFs can be estimated and restored images have richer details and less negative effects compared to state of art methods.Ĭonvex blind image deconvolution with inverse filteringīlind image deconvolution is the process of estimating both the original image and the blur kernel from the degraded image with only partial or no information about degradation and the imaging system. Second, we deal with image regions affected by the saturated pixels separately by modeling a weighted matrix during each multi-frame deconvolution iteration process. First, in the kernel estimation step, a light streak detection scheme using multi-frame blurred images is incorporated into the regularization constraint. In this paper, we propose a method to deal with the problem under the modified multi-frame blind deconvolution framework. When blurred images have saturated or over-exposed pixels, conventional blind deconvolution approaches often fail to estimate accurate point spread function (PSF) and will introduce local ringing artifacts. Ye, Pengzhao Feng, Huajun Xu, Zhihai Li, Qi Chen, Yueting Multi-frame partially saturated images blind deconvolution
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