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Image Enhancement by Gradient Distribution Specification

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9009))

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Abstract

We propose to use gradient distribution specification for image enhancement. The specified gradient distribution is learned from natural-scene image datasets. This enhances image quality based on two facts: First, the specified distribution is independent of image content. Second, the distance between the learned distribution and the empirical distribution of a given image correlates with subjectively perceived image quality. Based on those two facts, remapping an image such that the distribution of its gradients (and therefore also Laplacians) matches the specified distribution is expected to improve the quality of that image. We call this process “image naturalization”. Our experiments confirm that naturalized images are more appealing to visual perception. Moreover, “naturalness” can be used as a measure of image quality when ground-truth is unknown.

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Author information

Authors and Affiliations

  1. MOSAIC Group, Faculty of Computer Science, TU Dresden, Dresden, Germany

    Yuanhao Gong & Ivo F. Sbalzarini

  2. MOSAIC Group, Center for Systems Biology Dresden (CSBD), Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

    Yuanhao Gong & Ivo F. Sbalzarini

Authors
  1. Yuanhao Gong
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  2. Ivo F. Sbalzarini
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Corresponding author

Correspondence to Ivo F. Sbalzarini .

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Gong, Y., Sbalzarini, I.F. (2015). Image Enhancement by Gradient Distribution Specification. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-16631-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16630-8

  • Online ISBN: 978-3-319-16631-5

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