Main Article Content


Due to a variety of factors such as long time storage, dry environment, and volatilization of painting,
some cracks and stains may appear on the surface of the oil paintings. These tend to seriously affect the value of the paintings. Image restoration techniques are proving to be of great help in the analysis and documentation of our cultural heritage. Digital image restoration techniques provide a multitude of choices for improving the visual quality of images. In this work, we propose a multi-dimensional median filter and threshold algorithm for detection and removal of cracks in digital images. The work conducts an analysis of multi-dimensional median filter and threshold algorithm for effective restoration of cracks in digitized painting using the Java programming language version 8.0. To demonstrate the usefulness of this technique, cracked images of different resolution are collected for use in testing the efficiency of these models. The results show a remarkable difference between the original and enhanced images. This work is implemented using the Java programming language on Netbeans IDE. 


Digitized Paintings Crack Detection and Restoration Median Filter threshold Algorithm

Article Details

How to Cite
Ukpe, Kufre Christopher, & Ledisi Giok Kabari. (2021). DIGITIZED PAINTINGS FOR CRACK DETECTION AND RESTORATION USING MEDIAN FILTER AND THRESHOLD ALGORITHM . International Journal of Human Computing Studies, 3(4), 13-19.


  1. [1] Alexei L. (2005). Tips and Tricks: Fast image filtering algorithms. Moscow State University, Moscow, Russia.
  2. [2] Bovik A. C (1995). Digital image processing course notes. Dept. of Electrical Engineering, University of Texas at Austin.
  3. [3] Bhabatosh C, & Dwijest D. M. (2002). Digital image processing and analysis. PHI Learning Pvt Ltd.
  4. [4] Crocker, Lee Daniel (1995). PNG: The portable network graphic format. Dr. Dobb's Journal, 20 (232), 36–44.
  5. [5] Glenn R. & Thomas B. (1999). PNG: Portable network graphics Specification, Version 1.2. Massachusetts Institute of Technology (MIT).
  6. [6] Haralick R. M, and Shapiro L. G (1992). Computer and robot vision. Addison Wesley, Reading, MA.
  7. [7] Hummel R (1975). Histogram modification techniques. Computer Graphics and Image Processing, 4, 209-224.