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Low Light Image Enhancement via Sparse Representations

  • Konstantina Fotiadou
  • Grigorios Tsagkatakis
  • Panagiotis Tsakalides
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8814)

Abstract

Enhancing the quality of low light images is a critical processing function both from an aesthetics and an information extraction point of view. This work proposes a novel approach for enhancing images captured under low illumination conditions based on the mathematical framework of Sparse Representations. In our model, we utilize the sparse representation of low light image patches in an appropriate dictionary to approximate the corresponding day-time images. We consider two dictionaries; a night dictionary for low light conditions and a day dictionary for well illuminated conditions. To approximate the generation of low and high illumination image pairs, we generated the day dictionary from patches taken from well exposed images, while the night dictionary is created by extracting appropriate features from under exposed image patches. Experimental results suggest that the proposed scheme is able to accurately estimate a well illuminated image given a low-illumination version. The effectiveness of our system is evaluated by comparisons against ground truth images while compared to other methods for image night context enhancement, our system achieves better results both quantitatively as well as qualitatively.

Keywords

De-nighting Contrast enhancement Sparse representations 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Konstantina Fotiadou
    • 1
  • Grigorios Tsagkatakis
    • 1
  • Panagiotis Tsakalides
    • 1
  1. 1.Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH-ICS), Department of Computer ScienceUniversity of CreteHeraklion, CreteGreece

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