Digital Image Enhancement and Noise Filtering by Use of Local Statistics

  title={Digital Image Enhancement and Noise Filtering by Use of Local Statistics},
  author={Jong-Sen Lee},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both… CONTINUE READING
Highly Influential
This paper has highly influenced 148 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 2,722 citations. REVIEW CITATIONS
1,036 Citations
7 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 1,036 extracted citations

2,723 Citations

Citations per Year
Semantic Scholar estimates that this publication has 2,723 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-7 of 7 references

Image enhancement by histogram hyperbolization

  • W. Frei
  • Comput . Graphics Image Processing
  • 1977

A new approach to linear filtering and prediction problems

  • R. E. Kalman
  • Trans . ASME , J . Basic Eng .
  • 1960

A general image estimation algo - righm applicable to multiplicative and non - Gaussian noise

  • N. E. Nahi, M. Naraghi
  • Univ . of Southern California , Image Processing…

An approach to the space variant restoration and enhancement of images

  • R. Wallis
  • Proc . Symp . on Current Mathematical Problems in…

Baysian recursive image estimation

  • N. E. Nahi, T. Assefi
  • IEEE Trans . Comput .

Real time image enhancement techniques

  • R. W. Lowe, J. W. Weber

Similar Papers

Loading similar papers…