scholarly journals Deblurring of Motion Blurred Images Using GLCM and Elastic Net Regularization

2020 ◽  
pp. 411-414
Author(s):  
Reethika A ◽  
Vithya R ◽  
Kanivarshini S ◽  
Krishnakumar S ◽  
Priyadharshini A
Keyword(s):  
Haar Wavelet ◽  
Elastic Net ◽  
Gray Level ◽  
Edge Region ◽  
Running Time ◽  
Elastic Net Regularization ◽  
Edge Field ◽  
Blurred Image ◽  
Blurred Images ◽  
Region Information

An image deburring algorithm consists of rich edge area mining with a gray-level co-occurring matrix and elastic net regularisation is proposed in this paper. First, the luminance channel of an image is removed from the blurred image. The frequency layer is highthat can be derived from the blurred image by converting the 2D haar wavelet in the luminance channel.By the way, measurements were made using area and the richest edge region information is then collected. Finally, the extracted rich edge field, instead full motion blurred image, approximate the blur kernel elastic net regularisation and the image is returned. A measurement of image mechanism and running time measures the proposed system. Result suggestedto recommended strategy would improve efficiency and ensure continuity in recovery.

scholarly journals Restoration of Motion Blurred Images Based on Rich Edge Region Extraction Using a Gray-Level Co-Occurrence Matrix

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 15532-15540 ◽  
Author(s):  
Minghua Zhao ◽  
Xin Zhang ◽  
Zhenghao Shi ◽  
Peng Li ◽  
Bing Li
Keyword(s):  
Gray Level ◽  
Edge Region ◽  
Region Extraction ◽  
Occurrence Matrix ◽  
Blurred Images

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

scholarly journals Obscure Image Classification and Restoration using Support Vector Machines

2019 ◽  
Vol 8 (4) ◽  
pp. 8231-8236
Keyword(s):  
Kernel Estimation ◽  
Classification Algorithm ◽  
Support Vector ◽  
Svm Classification ◽  
Vector Machines ◽  
Blurred Image ◽  
Simulation Results ◽  
Blurred Images ◽  
Identification And Characterization ◽  
Restoration Model

A restoration and classification computation for blurred image which depends on obscure identification and characterization is proposed in this paper. Initially, new obscure location calculation is proposed to recognize the Gaussian, Motion and Defocus based blurred locales in the image. The degradation-restoration model referred with pre-processing followed by binarization and features extraction/classification algorithm applied on obscure images. At this point, support vector machine (SVM) classification algorithm is proposed to cluster the blurred images. Once the obscure class of the locales is affirmed, the structure of the obscure kernels of the blurred images are affirmed. At that point, the obscure kernel estimation techniques are embraced to appraise the obscure kernels. At last, the blurred locales are re-established utilizing nonblind image deblurring calculation and supplant the blurred images with the restored images. The simulation results demonstrate that the proposed calculation performs well

Restoration of Motion Blurred Image Based on PSO Combine Wiener Filter in Ship Imaging System

2014 ◽  
Vol 1006-1007 ◽  
pp. 739-742
Author(s):  
Hui Xuan Fu ◽  
Yu Chao Wang ◽  
Xun Su
Keyword(s):  
Imaging System ◽  
Search Algorithm ◽  
Wiener Filter ◽  
Optimal Solution ◽  
Parameter Selection ◽  
Improve Image Quality ◽  
Blurred Image ◽  
Blurred Images ◽  
Ringing Effect

Ship internal equipment vibration will cause the imaging system platform vibration, resulting in blurred images. Wiener Filter is often used to restore the motion blurred image. The principle of the method expects to minimize the mean square error between the restore image and original image. However, this method has some constrains, if parameter selection improper, it generates ringing effect easily. Usually, most users select parameter by rule of thumb, so they frequently fail to generate the optimal solution. In order to get high quality restore image, eliminate the ringing effect, a new approach based on particle swarm optimization (PSO) Wiener Filter was proposed, which automatically adjusts the parameter for Wiener Filter, this method seek the optimal solution by transferring information between individuals and information sharing, which is a highly efficient parallel search algorithm, insuring the accuracy of parameter selection, effectively reducing the ringing effect after image restoration, improve image quality of restoration.

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