![]() These researches can be roughly divided into two categories: blind and non-blind deblurring. Early research on motion deblurring are mostly focusing on proposing effective algorithm to inverse the process of image degradation, i.e., deconvolution of the blurred image. In general, the motion blurred image restoration techniques are used to eliminate or minimize the impact of PSF from the degraded image. ![]() Intuitively, the motion blurred image can be usually modeled as a convolution of the original image with PSF, which can also be named blur kernel. A popular way to tackle motion deblurring of single image is to deconvolute the blurred image with PSF. ![]() However, these efficient infrastructures are not suitable to the application of image deblurring, since the research theory of image restoration is different from other image processing areas. partial differential equation (PDE), multi-sink distributed power control algorithm (MSDPC-SRMS), wireless sensor networks, wireless mesh networks (WMNs), hidden Markov model (HMM) and ALOHA protocol. In addition, some researchers have proposed efficient infrastructures to increase the operational efficiency of image processing applications, e.g. Two kinds of deep learning methods are either time-consuming to calculate the complex training structure or have special requirements for blur conditions, both of them are not suitable for single image deblurring. Even though Gabor filter and RBFNN work well on the estimation of PSF parameters, they require sufficient Gabor filter masks in various orientations to ensure its accuracy. Dash has developed a Gabor filter and radial basis function neural network (RBFNN) to estimate the blur parameters in frequency response. The first kinds of methods rely on multi-frame images, and they own a complex network structure, so they are time-consuming the second types of these approaches only use single image to deblur the degraded image, this kind of method is simple in network structure and fast in training, but they still have some shortcomings: Aizenberg developed a multi-layer neural network (MLMVN) to conduct blur identification, however, MLMVN is concentrated almost on horizontal blur. These deep learning approaches can be classified into two categories. Some of the latest methods adopt deep learning to predict the probabilistic distribution of motion blur and recover the degraded images. So efficient motion deblurring technology is conducive to improving the reliability of related applications, such as aerospace, medical imaging, traffic monitoring, public safety, military search, satellite and space image. As one of the main causes of image degradation, it seriously affects the performances of computer vision system in various fields. Motion blurring is generated inevitably by camera shake during exposure time. 16JK1775).Ĭompeting interests: The authors have declared that no competing interests exist. 2019GY-215) and The Scientific Research Program of Education Department of Shaanxi Provincial Government (No. 2017YFB1402104), and was partly supported by The Key Research and Development Program of Shaanxi Province (No. 2020KW-068) and The National Key Research and Development Program of China (No. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the manuscript and VOC2012 public repository( ).įunding: This research was equally core-funded by The Key Research and Development Program of Shaanxi Province (No. Received: Accepted: AugPublished: September 1, 2020Ĭopyright: © 2020 Zhou et al. (2020) Improved estimation of motion blur parameters for restoration from a single image. Citation: Zhou W, Hao X, Wang K, Zhang Z, Yu Y, Su H, et al.
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