Image Interpolation via Gradient Correlation-Based Edge Direction Estimation
This paper introduces an image interpolation method that provides performance superior to that of the state-of-the-art algorithms. The simple linear method, if used for interpolation, provides interpolation at the cost of blurring, jagging, and other artifacts; however, applying complex methods prov...
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Series: | Scientific Programming |
Online Access: | http://dx.doi.org/10.1155/2020/5763837 |
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doaj-1f198f95c418424dbe15bd64df660f162021-07-02T09:46:09ZengHindawi LimitedScientific Programming1058-92441875-919X2020-01-01202010.1155/2020/57638375763837Image Interpolation via Gradient Correlation-Based Edge Direction EstimationSajid Khan0Dong-Ho Lee1Muhammad Asif Khan2Muhammad Faisal Siddiqui3Raja Fawad Zafar4Kashif Hussain Memon5Ghulam Mujtaba6Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Sukkur IBA University, Sukkur, PakistanSchool of Electrical Engineering, Hanyang University ERICA Campus, Ansan, Republic of KoreaDepartment of Electrical Engineering, Sukkur IBA University, Sukkur, PakistanFaculty of Engineering, Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Mathematics and Social Sciences, Sukkur IBA University, Sukkur, PakistanSchool of Electrical Engineering, Hanyang University ERICA Campus, Ansan, Republic of KoreaCenter of Excellence for Robotics, Artificial Intelligence, and Blockchain, Sukkur IBA University, Sukkur, PakistanThis paper introduces an image interpolation method that provides performance superior to that of the state-of-the-art algorithms. The simple linear method, if used for interpolation, provides interpolation at the cost of blurring, jagging, and other artifacts; however, applying complex methods provides better interpolation results, but sometimes they fail to preserve some specific edge patterns or results in oversmoothing of the edges due to postprocessing of the initial interpolation process. The proposed method uses a new gradient-based approach that makes an intelligent decision based on the edge direction using the edge map and gradient map of an image and interpolates unknown pixels in the predicted direction using known intensity pixels. The input image is subjected to the efficient hysteresis thresholding-based edge map calculation, followed by interpolation of low-resolution edge map to obtain a high-resolution edge map. Edge map interpolation is followed by classification of unknown pixels into obvious edges, uniform regions, and transitional edges using the decision support system. Coefficient-based interpolation that involves gradient coefficient and distance coefficient is applied to obvious edge pixels in the high-resolution image, whereas transitional edges in the neighborhood of an obvious edge are interpolated in the same direction to provide uniform interpolation. Simple line averaging is applied to pixels that are not detected as an edge to decrease the complexity of the proposed method. Applying line averaging to smooth pixels helps to control the complexity of the algorithm, whereas applying gradient-based interpolation preserves edges and hence results in better performance at reasonable complexity.http://dx.doi.org/10.1155/2020/5763837 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sajid Khan Dong-Ho Lee Muhammad Asif Khan Muhammad Faisal Siddiqui Raja Fawad Zafar Kashif Hussain Memon Ghulam Mujtaba |
spellingShingle |
Sajid Khan Dong-Ho Lee Muhammad Asif Khan Muhammad Faisal Siddiqui Raja Fawad Zafar Kashif Hussain Memon Ghulam Mujtaba Image Interpolation via Gradient Correlation-Based Edge Direction Estimation Scientific Programming |
author_facet |
Sajid Khan Dong-Ho Lee Muhammad Asif Khan Muhammad Faisal Siddiqui Raja Fawad Zafar Kashif Hussain Memon Ghulam Mujtaba |
author_sort |
Sajid Khan |
title |
Image Interpolation via Gradient Correlation-Based Edge Direction Estimation |
title_short |
Image Interpolation via Gradient Correlation-Based Edge Direction Estimation |
title_full |
Image Interpolation via Gradient Correlation-Based Edge Direction Estimation |
title_fullStr |
Image Interpolation via Gradient Correlation-Based Edge Direction Estimation |
title_full_unstemmed |
Image Interpolation via Gradient Correlation-Based Edge Direction Estimation |
title_sort |
image interpolation via gradient correlation-based edge direction estimation |
publisher |
Hindawi Limited |
series |
Scientific Programming |
issn |
1058-9244 1875-919X |
publishDate |
2020-01-01 |
description |
This paper introduces an image interpolation method that provides performance superior to that of the state-of-the-art algorithms. The simple linear method, if used for interpolation, provides interpolation at the cost of blurring, jagging, and other artifacts; however, applying complex methods provides better interpolation results, but sometimes they fail to preserve some specific edge patterns or results in oversmoothing of the edges due to postprocessing of the initial interpolation process. The proposed method uses a new gradient-based approach that makes an intelligent decision based on the edge direction using the edge map and gradient map of an image and interpolates unknown pixels in the predicted direction using known intensity pixels. The input image is subjected to the efficient hysteresis thresholding-based edge map calculation, followed by interpolation of low-resolution edge map to obtain a high-resolution edge map. Edge map interpolation is followed by classification of unknown pixels into obvious edges, uniform regions, and transitional edges using the decision support system. Coefficient-based interpolation that involves gradient coefficient and distance coefficient is applied to obvious edge pixels in the high-resolution image, whereas transitional edges in the neighborhood of an obvious edge are interpolated in the same direction to provide uniform interpolation. Simple line averaging is applied to pixels that are not detected as an edge to decrease the complexity of the proposed method. Applying line averaging to smooth pixels helps to control the complexity of the algorithm, whereas applying gradient-based interpolation preserves edges and hence results in better performance at reasonable complexity. |
url |
http://dx.doi.org/10.1155/2020/5763837 |
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