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Recovering Images Using Image Inpainting Techniques

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Robotics, Control and Computer Vision

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1009))

Abstract

Image inpainting is the task of reconstructing corrupted images. This challenging task has been tackled by various approaches. Due to recent advancements in image processing techniques more sophisticated approaches are available for image inpainting. Deep Convolutional Neural Networks and Generative Adversarial Networks are proven to be effective for this task. Image inpainting is used to restore images damaged due to scratches, remove unwanted objects while editing, recover the style of old photographs, effectively encode images for transmission, and many more. This work identifies the methods available for the image inpainting tasks and analyzes their effectiveness on established metrics. Navier–Stokes and Telea algorithms achieve PSNR 32 between 34 and SSIM above 0.96 for smaller inpainting tasks. Telea algorithm performs better as compared to Navier–Stokes method.

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Authors and Affiliations

  1. Department of Computer Engineering and IT, College of Engineering, Pune (COEP), 411005, Pune, Maharashtra, India

    Soureesh Patil, Amit Joshi & Suraj Sawant

Authors
  1. Soureesh Patil
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  2. Amit Joshi
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  3. Suraj Sawant
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Corresponding author

Correspondence to Soureesh Patil .

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Editors and Affiliations

  1. National Institute of Technology Uttarakhand, Srinagar, India

    Hariharan Muthusamy

  2. Eötvös Loránd University, Budapest, Hungary

    János Botzheim

  3. Queensland University of Technology, Brisbane, QLD, Australia

    Richi Nayak

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Patil, S., Joshi, A., Sawant, S. (2023). Recovering Images Using Image Inpainting Techniques. In: Muthusamy, H., Botzheim, J., Nayak, R. (eds) Robotics, Control and Computer Vision. Lecture Notes in Electrical Engineering, vol 1009. Springer, Singapore. https://doi.org/10.1007/978-981-99-0236-1_3

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