DIGITAL IMAGE RESTORATION USING SURF ALGORITHM
Shanmukhaprasanthi Tammineni
prashanthitammineni.rs@andhrauniversity.edu.inAndhra University College of Engineering, Department of Electronics and Communication Engineering (India)
https://orcid.org/0009-0000-5352-2265
Swaraiya Madhuri Rayavarapu
Andhra University College of Engineering, Department of Electronics and Communication Engineering (India)
https://orcid.org/0009-0007-7559-2142
Sasibhushana Rao Gottapu
Andhra University College of Engineering, Department of Electronics and Communication Engineering (India)
https://orcid.org/0000-0002-0416-2918
Raj Kumar Goswami
Gayatri Vidya Parishad College of Engineering for Women, Departement of Electronics and Communication Engineering (India)
https://orcid.org/0000-0002-0651-6783
Abstract
In contemporary times, the preservation of scientific and creative endeavours often relies on the utilization of film and image archives, hence emphasizing the significance of image processing as a critical undertaking. Image inpainting refers to the process of digitally altering an image in a manner that renders the adjustments imperceptible to a viewer lacking knowledge of the original image. Image inpainting is a technique mostly employed to restore damaged regions within an image by utilizing information obtained from matching characteristics in relevant images. This process involves filling in the damaged areas and removing undesired objects. The SURF (Speeded Up Robust Feature) algorithm under consideration is partitioned into three primary phases. Firstly, the essential characteristics of the impaired image and the pertinent image are identified. In the second stage, the relationship between the damaged image and the relevant image is determined in terms of translation, scaling, and rotation. Ultimately, the destroyed area is reconstructed through the application of the inverse transformation. The quality assessment of inpainted images can be evaluated using metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). The experimental findings provide evidence that the suggested inpainting technique is effective in terms of both speed and quality.
Keywords:
SURF, inpainting, image restoration, image featuresReferences
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Authors
Shanmukhaprasanthi Tammineniprashanthitammineni.rs@andhrauniversity.edu.in
Andhra University College of Engineering, Department of Electronics and Communication Engineering India
https://orcid.org/0009-0000-5352-2265
Authors
Swaraiya Madhuri RayavarapuAndhra University College of Engineering, Department of Electronics and Communication Engineering India
https://orcid.org/0009-0007-7559-2142
Authors
Sasibhushana Rao GottapuAndhra University College of Engineering, Department of Electronics and Communication Engineering India
https://orcid.org/0000-0002-0416-2918
Authors
Raj Kumar GoswamiGayatri Vidya Parishad College of Engineering for Women, Departement of Electronics and Communication Engineering India
https://orcid.org/0000-0002-0651-6783
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