scholarly journals IMAGE SHARPENING WITH BLUR MAP ESTIMATION USING CONVOLUTIONAL NEURAL NETWORK

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 161-166
Author(s):  
A. Nasonov ◽  
A. Krylov ◽  
D. Lyukov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Map Estimation ◽  
Image Sharpening ◽  
Optimal Values ◽  
Blur Kernel ◽  
Blur Map

<p><strong>Abstract.</strong> We propose a method for choosing optimal values of the parameters of image sharpening algorithm for out-of-focus blur based on grid warping approach. The idea of the considered sharpening algorithm is to move pixels from the edge neighborhood towards the edge centerlines. Compared to traditional deblurring algorithms, this approach requires only scalar blur level value rather than a blur kernel. We propose a convolutional neural network based algorithm for estimating the blur level value.</p>

Motion-blur kernel size estimation via learning a convolutional neural network

2019 ◽  
Vol 119 ◽  
pp. 86-93 ◽  
Author(s):  
Lerenhan Li ◽  
Nong Sang ◽  
Luxin Yan ◽  
Changxin Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motion Blur ◽  
Size Estimation ◽  
Kernel Size ◽  
Blur Kernel

scholarly journals A PSD Maps Estimation Algorithm for Underlay Cognitive Radio Networks Based on the Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xu Han ◽  
Lei Xue ◽  
Ying Xu
Keyword(s):  
Neural Network ◽  
Cognitive Radio ◽  
Convolutional Neural Network ◽  
Cognitive Radio Networks ◽  
Estimation Algorithm ◽  
Radio Networks ◽  
Map Estimation ◽  
Color Mapping ◽  
Underlay Cognitive Radio ◽  
Conventional Methods

In the underlay cognitive radio networks (CRNs), the power spectral density (PSD) maps play a foundational role in detecting the idle radio resources. However, it is hard to get a high-accurate PSD map estimation result because of the complicated radio environment. For this reason, we propose a novel convolutional neural network- (CNN-) based PSD map estimation algorithm named map reconstruction CNN (MRCNN). Using the CNN to estimate PSD maps for underlay CRNs has not been reported until now. First, on the basis of the proposed color mapping process, we transform the PSD map estimation task to the image reconstruction task. Then, we train the MRCNN to learn the radio environment characteristics from the training data, rather than making direct biased or imprecise wireless environment hypotheses as in the conventional methods. We utilize the extracted knowledge in the training process to reconstruct the PSD map images. As demonstrated in the simulations, the proposed MRCNN method has a better PSD map estimation performance than the conventional methods.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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