Noise Elimination

See how to execute a Noise Elimination in the SPRING.

In the image generation process, some noise are inserted in the images. Generally, the pixels with noise show up as points, with gray levels very different than its neighborhood (dark (black) or saturated (white)). These noisy points can be distributed randomly or in a systematic way (vertical or horizontal lines). The reasons for this can be a detector fail, sensor electronic system limitations, among others.

Thus, the Noise Elimination function in the SPRING, has the purpose to eliminate or reduce the image noisy points.

The algorithm uses two limits: the Inferior Limit and the Superior Limit. The user has to select the limit values to be used in the noisy points characterization. Before the function execution, it is recommended that the user performs a previous analysis of the image noise, using the SPRING function, Pixels Reading. This analysis allow the user to select the adequate limits for the noise level present in the image.

To detect, if a point P(i,j) (row "i ", and column"j") in the image is or not a noisy point, only the superior neighbors P(i-1,j) and inferior neighbors P(i+1,j) will be analyzed.

Select the Inferior Limit

A point will be considered noise if its gray level is below the gray level of its two neighbors, above and below (above and below lines) by a difference greater than the inferior limit. In this case the point will be substituted by the average of those two neighbor points. The "default" value for this parameter is 8.

Select the Superior Limit

A point will be considered noise if its gray level is above the gray level of its two neighbors, above and below (above and below lines) by a difference greater than the superior limit. In this case the point will be substituted by the average of those two neighbor points. The "default" value for this parameter is 25.

Accessing the Noise Elimination function in the SPRING main menu, is through the Image item in the Menu bar. To operate this function an image has to be visualized in the window.

See also:

Other Image Processing techniques.
How to execute Noise Elimination.