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GRNN Based an Intelligent Technique for Image Inpainting

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Machine Learning Algorithms for Industrial Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 907))

Abstract

Image inpainting is usually framed as a constrained image generation problem. It is a method that helps to reconstruct the lost or deteriorated parts of images as well as video. The main focus in image inpainting techniques is how precisely to can generate the corrupted pixels in an image. In this paper, we tried using a single pass learning algorithm which greatly reduce time to train the model. The objective of the proposed model is to reconstruct large continuous regions of missing or deteriorated parts of an image. In this paper, GRNN based model along with some image inpainting techniques is being used. Each image is divided into two sections: the missing part that is to be reconstructed, and the context. The network would work identically for arbitrary removals not just for regions having particular shapes such as square or rectangles. Final evaluation is based on the Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) between the corrupted image and the original image for the regions which is to be regenerate.

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Authors and Affiliations

  1. School of Computer Science and Engineering, National Institute of Science and Technology (Autonomous), Institute Park, Pallur Hills, Berhampur, 761008, Odisha, India

    Debanand Kanhar & Raunak Chandak

Authors
  1. Debanand Kanhar
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  2. Raunak Chandak
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Correspondence to Raunak Chandak .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, National Institute of Science and Technology (Autonomous), Berhampur, Odisha, India

    Santosh Kumar Das

  2. School of Computer Science and Engineering, National Institute of Science and Technology (Autonomous), Berhampur, Odisha, India

    Shom Prasad Das

  3. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  4. Founder and Head of the Egyptian Scientific Research Group (SRGE), Information Technology Department, Cairo University, Faculty of Computer and Artificial Intelligence, Giza, Egypt

    Aboul-Ella Hassanien

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Kanhar, D., Chandak, R. (2021). GRNN Based an Intelligent Technique for Image Inpainting. In: Das, S., Das, S., Dey, N., Hassanien, AE. (eds) Machine Learning Algorithms for Industrial Applications. Studies in Computational Intelligence, vol 907. Springer, Cham. https://doi.org/10.1007/978-3-030-50641-4_10

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