Text Enhancement in Projected Imagery

  • Xiaodan Hu
  • Mohamed A. Naiel
  • Zohreh Azimifar
  • Ibrahim Ben Daya
  • Mark Lamm
  • Paul Fieguth


There is great interest in improving the visual quality of projected
imagery. In particular, for image enhancement, we would assert
that text and non-text regions should be enhanced differently in
seeking to maximize perceived quality, since the spatial and statis-
tical characteristics of text and non-text images are quite distinct.
In this paper, we present a text enhancement scheme based on a
novel local dynamic range statistical thresholding. Given an input
image, text-like regions are obtained on the basis of computing the
local statistics of regions having a high dynamic range, allowing a
pixel-wise classification into text-like or background classes. The
actual enhancement is obtained via class-dependent Wiener filter-
ing, with text-like regions sharpened more than the background.
Experimental results on four challenging images show that the pro- 
posed scheme offers a better visual quality than projection with- 
out enhancement as well as a recent state-of-the-art enhancement

How to Cite
Hu, X., Naiel, M., Azimifar, Z., Ben Daya, I., Lamm, M., & Fieguth, P. (2018). Text Enhancement in Projected Imagery. Journal of Computational Vision and Imaging Systems, 4(1), 3. Retrieved from https://openjournals.uwaterloo.ca/index.php/vsl/article/view/331