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International Conference on Artificial Neural Networks

ICANN 2019: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing pp 3–16Cite as

Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11729))

Abstract

Image enhancement refers to the enrichment of certain image features such as edges, boundaries, or contrast. The main objective is to process the original image so that the overall performance of visualization, classification and segmentation tasks is considerably improved. Traditional techniques require manual fine-tuning of the parameters to control enhancement behavior. To date, recent Convolutional Neural Network (CNN) approaches frequently employ the aforementioned techniques as an enriched pre-processing step. In this work, we present the first intrinsic CNN pre-processing layer based on the well-known unsharp masking algorithm. The proposed layer injects prior knowledge about how to enhance the image, by adding high frequency information to the input, to subsequently emphasize meaningful image features. The layer optimizes the unsharp masking parameters during model training, without any manual intervention. We evaluate the network performance and impact on two applications: CIFAR100 image classification, and the PlantCLEF identification challenge. Results obtained show a significant improvement over popular CNNs, yielding 9.49% and 2.42% for PlantCLEF and general-purpose CIFAR100, respectively. The design of an unsharp enhancement layer plainly boosts the accuracy with negligible performance cost on simple CNN models, as prior knowledge is directly injected to improve its robustness.

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Notes

  1. 1.

    https://github.com/maeotaku/pytorch_usm.

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Author information

Authors and Affiliations

  1. Instituto Tecnologico de Costa Rica, PAttern Recognition and MAchine Learning Group, Cartago, Costa Rica

    Jose Carranza-Rojas, Saul Calderon-Ramirez, Adán Mora-Fallas & Michael Granados-Menani

  2. Rovira i Virgili University, Tarragona, Spain

    Jordina Torrents-Barrena

Authors
  1. Jose Carranza-Rojas
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  2. Saul Calderon-Ramirez
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  3. Adán Mora-Fallas
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  4. Michael Granados-Menani
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  5. Jordina Torrents-Barrena
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Corresponding author

Correspondence to Saul Calderon-Ramirez .

Editor information

Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Praha 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Carranza-Rojas, J., Calderon-Ramirez, S., Mora-Fallas, A., Granados-Menani, M., Torrents-Barrena, J. (2019). Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-30508-6_1

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  • Online ISBN: 978-3-030-30508-6

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