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Volume: 27 | Article ID: art00065
Deep Learning Approaches for Whiteboard Image Quality Enhancement
  DOI :  10.2352/J.ImagingSci.Technol.2019.63.4.040404  Published OnlineJuly 2019

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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Mekides Assefa Abebe, Jon Yngve Hardeberg, "Deep Learning Approaches for Whiteboard Image Quality Enhancementin Proc. IS&T 27th Color and Imaging Conf.,  2019,  pp 360 - 368,

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Copyright © Society for Imaging Science and Technology 2019
Color and Imaging Conference
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Society for Imaging Science and Technology