Image Restoration Black Magic

These are examples of an image restoration technique applicable even when as much as 85% of the pixels are corrupted. or unknown, as in a sparse image. The examples are of some general interest, but were placed on the Web to aid image engineering researchers. The technical details of the algorithm used is briefly outlined in the paper, Harvey A. Cohen, "Image Restoration via N-Nearest Neighbour Classification", Proc. 1996 IEEE International Conference on Image Processing, Lausanne, Switzerland, 16-19 Sept 1996, Vol 1, pp 1005-1008. This is available as Paper 96:10 in my Paper Download list.
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Click on the thumbnails to view the specified image.

Clown 85% corrupted with black impulse noise

Clown 85% corrupted with black impulse restored

Clown Image uncorrupted

Clown 85% corrupted with white impulse noise restored

The restoration algorithm used requires the determination of the distance of each corrupt pixel from the nearest good pixels, using Rosenfeld-Pfaltz distance operator.
The pretty random wallpapers produced during this process are part of the story on the page on Distance Transformation Magic, Auras, and Gobblegook.

Lena 85% corrupted FD - BK as per paper

Lena 85% corrupted FD as per paper

Lena 85% corrupted white noise

Lena 85% corrupted Restored

Lena Image Original

Harvey Cohen's Home Page
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