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Light source detection for digital images in noisy scenes: a neural network approach

  • Computational Intelligence for Vision and Robotics
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Abstract

To produce realistic synthetic images, it is important to shade objects based on real illumination conditions of a scene. Estimating the direction of the light source of the scene is a key factor to achieve this. Properly estimating this source under noisy conditions is very challenging, and it is a subject of intense research. Computational intelligence techniques offer promising way of tackling this problem. This paper presents a novel neural network-based approach for recovering light source direction in relation to the viewpoint direction of a graphical image in noisy environments. The estimated light source direction can be used for the generation of 3D images from 2D ones. Experiments are performed using both synthetic and real images in noisy scenes. Four synthetic surfaces where generated with varying light source directions for a total of 12 images. Three real images were also used with varying degrees of noise. The experimental results show that the proposed approach is robust and provides a good level of accuracy.

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Authors and Affiliations

  1. DIGITS, Faculty of Technology De Montfort University, Leicester, LE19BH, UK

    David A. Elizondo

  2. National Centre for Population Health and Wellbeing Research, Swansea University Medical School, Singleton Park, Swansea, SA2 8PP, UK

    Shang-Ming Zhou

  3. Department of Genetics, University of Leicester, Adrian Bld., University Rd., Leicester, LE17RH, UK

    Charalambos Chrysostomou

Authors
  1. David A. Elizondo
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  2. Shang-Ming Zhou
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  3. Charalambos Chrysostomou
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Correspondence to David A. Elizondo.

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Elizondo, D.A., Zhou, SM. & Chrysostomou, C. Light source detection for digital images in noisy scenes: a neural network approach. Neural Comput & Applic 28, 899–909 (2017). https://doi.org/10.1007/s00521-016-2281-0

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  • DOI: https://doi.org/10.1007/s00521-016-2281-0

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