Noise removal and enhancement of binary images using morphological operations

Mathematical morphological operations are commonly used as a tool in image processing for extracting image components that are useful in the representation and description of region shape. In this paper, six basic morphological operations are investigated to remove noise and enhance the appearance of binary images. Dilation, erosion, opening, closing, fill and majority operations are tested on twenty-five images and subjectively evaluated based on perceived quality of the enhanced images. Results of the experiments showed that noise can be effectively removed from binary images using combinations of erode-dilate operations. Also, the binary images are significantly enhanced using combinations of majority-close operations.

[1]  E.J. Delp,et al.  The use of mathematical morphology in image enhancement , 1989, Proceedings of the 32nd Midwest Symposium on Circuits and Systems,.

[2]  Azriel Rosenfeld,et al.  Computer vision and image processing , 1992 .

[3]  Richard Alan Peters,et al.  A new algorithm for image noise reduction using mathematical morphology , 1995, IEEE Trans. Image Process..

[4]  Constantine Kotropoulos,et al.  Morphological signal adaptive median filter for noise removal , 1996, Proceedings of Third International Conference on Electronics, Circuits, and Systems.

[5]  Kristel Michielsen,et al.  Morphological image analysis , 2000 .

[6]  Abdelmalik Taleb-Ahmed,et al.  Semi-automatic segmentation of vessels by mathematical morphology: application in MRI , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[7]  Hanqing Lu,et al.  SAR image enhancement technique based on morphological wavelet transformation , 2001, International Symposium on Multispectral Image Processing and Pattern Recognition.

[8]  Tim Morris,et al.  Computer Vision and Image Processing: 4th International Conference, CVIP 2019, Jaipur, India, September 27–29, 2019, Revised Selected Papers, Part I , 2020, CVIP.

[9]  Nursuriati Jamil,et al.  Gradient-Based Edge Detection of Songket Motifs , 2003, ICADL.

[10]  N. Jamil,et al.  A comparison of noise removal techniques in songket motif images , 2004 .

[11]  Li Qi,et al.  Reducing periodic noise using soft morphology filter , 2004 .

[12]  Zainab Abu Bakar,et al.  A comparison of noise removal techniques in songket motif images , 2004, Proceedings. International Conference on Computer Graphics, Imaging and Visualization, 2004. CGIV 2004..

[13]  Jian-nan Chi,et al.  Algorithm of image enhancement based on order morphological filtering and image entropy difference , 2005, International Symposium on Multispectral Image Processing and Pattern Recognition.

[14]  M. Wirth,et al.  Selective image enhancement using attribute morphology , 2005 .

[15]  Jian-Nan Chi,et al.  Algorithm of image enhancement based on order morphology filtering and image entropy difference , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[16]  Francisco Ortiz.  Gaussian Noise Removal by Color Morphology and Polar Color Models , 2006, ICIAR.