Comparative Study of OpenCV Inpainting Algorithms

Comparative Study of OpenCV Inpainting Algorithms

Preeti Chatterjee

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Subhadeep Jana

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Souradeep Ghosh

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Comparative Study of OpenCV Inpainting Algorithms

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Abstract

Digital image processing has been a significant and important part in the realm of computing science since its inception. It entails the methods and techniques that are used to manipulate a digital image using a digital computer. It is a type of signal processing in which the input and output maybe image or features/characteristics associated with that image. In this age of advanced technology, digital image processing has its uses manifold, some major fields being image restoration, medical field, computer vision, color processing, pattern recognition and video processing. Image inpainting is one such important domain of image processing. It is a form of image restoration and conservation. This paper presents a comparative study of the various digital inpainting algorithms provided by Open CV (a popular image processing library) and also identifies the most effective inpainting algorithm on the basis of Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and runtime metrics.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Preeti Chatterjee. 2021. \u201cComparative Study of OpenCV Inpainting Algorithms\u201d. Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 21 (GJCST Volume 21 Issue G2): .

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GJCST Volume 21 Issue G2
Pg. 27- 37

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-G Classification: B.2.4

Submission ReceivedSeptember 6, 2021
Peer Review Double Blind
Handling Editor
Accepted October 2, 2021
Published October 15, 2021

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v1.2

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August 20, 2021

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