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Demosaicing Using MFIT (Matrix Factorization Iterative Tunable) Based on Convolution Neural Network

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Intelligent Data Communication Technologies and Internet of Things (ICICI 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 38))

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Abstract

Demosaicing is introduced for estimation of the other missing color for producing the absolute color image. Enormous research has been done in past to develop the Demosaicing algorithm, however all these methods have shortcomings, hence in this paper, we propose an algorithm called MF-IT (Matrix Factorization Iterative Tunable), this methodology is based on the CNN (Convolution Neural Network), the main aim of this research is to improvise the reconstruction quality of the given image. This is achieved by using the image-block adjustments based transform, which helps in utilizing the image block based transform. One of the main advantage of MFIT is shift invariance, which can be easily obtained without any support of striding image blocks. Henceforth, In order to evaluate the performance of our methodology it is compared against the various state-of-art method. The result analysis shows that proposed methodology simply outperforms the other method.

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Author information

Authors and Affiliations

  1. Department of E&CE, KBN College of Engineering, Kalaburagi, 585104, India

    Shabana Tabassum

  2. Department of E&CE, BKIT, Bhalki, 585328, India

    SanjayKumar C. Gowre

Authors
  1. Shabana Tabassum
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  2. SanjayKumar C. Gowre
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Corresponding author

Correspondence to Shabana Tabassum .

Editor information

Editors and Affiliations

  1. Department of ECE, Karunya University, Coimbatore, India

    D. Jude Hemanth

  2. Department of Electronics and Computer Engineering, Tribhuvan University, Kirtipur, Nepal

    Subarna Shakya

  3. School of Science, Edith Cowan University, Joondalup Campus, Joondalup, WA, Australia

    Zubair Baig

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Tabassum, S., Gowre, S.C. (2020). Demosaicing Using MFIT (Matrix Factorization Iterative Tunable) Based on Convolution Neural Network. In: Hemanth, D., Shakya, S., Baig, Z. (eds) Intelligent Data Communication Technologies and Internet of Things. ICICI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-34080-3_29

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