Comparative study of image filters.

1. Image filtering: A Comparative
Study
-Prutha Bhalde(506101)
-Yogita Thorat(506118)

2. Objective
▫ To avoid signal weakening (objects counters and
edges blurred).
▫ To make sure non-corrupted (good) image pixels
are left intact, irrespective of density of noise in
the image.
▫ To avoid the situation where detected noise pixel
is replaced with another noise pixel.

3. Literature Servey
• Two pass median filter for impulse noise removal:
(Anna Fabijanska, 2009)
• Salt-and-Pepper Noise Removal by Median-type
Noise Detectors and Detail-preserving
Regularization (Raymond H. Chan, Chung-Wa Ho,
and Mila Nikolova, 2004)
• Improved median filtering algorithm for the
reduction of impulse noise in corrupted 2d greyscale
images (weyori, benjamin asubam, 2011)
• Noise Adaptive Soft-Switching Median Filter (How-
Lung and Kai-Kuang Ma, IEEE, 2001)

4. System Architecture

5. Types of filters used
• Median Filter
• Adaptive Median Filter
• Adaptive Center weighted median Filter
• Tri-State Median Filter

6. Original Image

7. Median Filter Image

8. Adaptive Median Filter Image

9. Central Median Filter Image

10. Conclusion
• The system design is proposed for the comparative
study of various filters used today for the image
filtrations. There are various filters used in the systems
such as median filter, adaptive median filter, adaptive
center weighted median filter and tri-state median filter.
• The study of all the filters states that as we use more
detailed filtering, smoothing effect increases and the
details of the images are reduced. So we tried to use
adaptive median filter and center weighted median filter
to overcome this drawback and increase the efficiency of
the filters.
• The effects can be used in many areas of study such
as image smoothing in the field of medical science,
geographical study, mobile applications, image
processing systems, space research study etc.