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ECASR: Efficient Channel Attention Based Super-Resolution

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Computer Vision and Image Processing (CVIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1376))

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Abstract

Despite recent advancements in single image super-resolution (SISR) methodologies, reconstruction of photo-realistic high resolution (HR) image from its single low resolution (LR) counterpart remains a challenging task in the fraternity of computer vision. In this work, we approach the problem of SR using a modified GAN with specialized Efficient Channel Attention (ECA) mechanism. CA mechanism prioritizes convolution channels according to there importance. The ECA mechanism, an extension of CA, improves model performance and decreases the complexity of learning. To capture the image texture accurately low-level features are used for reconstruction along with high-level features. A dual discriminator is used with GAN to achieve high perceptual quality. The experimental result shows that the proposed method produces better results for most of the dataset, in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and mean-opinion-score (MOS) over the state-of-the-art methods on benchmark data-sets when trained with same parameters.

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Authors and Affiliations

  1. IIIT Guwahati, Guwahati, India

    Sameeran Borah & Nilkanta Sahu

Authors
  1. Sameeran Borah
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  2. Nilkanta Sahu
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Corresponding author

Correspondence to Nilkanta Sahu .

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Editors and Affiliations

  1. Indian Institute of Information Technology Allahabad, Prayagraj, India

    Satish Kumar Singh

  2. Indian Institute of Technology Roorkee, Roorkee, India

    Partha Roy

  3. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  4. Indian Institute of Information Technology Allahabad, Prayagraj, India

    P. Nagabhushan

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Borah, S., Sahu, N. (2021). ECASR: Efficient Channel Attention Based Super-Resolution. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1376. Springer, Singapore. https://doi.org/10.1007/978-981-16-1086-8_33

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  • DOI: https://doi.org/10.1007/978-981-16-1086-8_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1085-1

  • Online ISBN: 978-981-16-1086-8

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