Abstract
Median filtering is a commonly used technique in image processing. The main problem of the median filter is its high computational cost (for sorting N pixels, the temporal complexity is O(N·log N), even with the most efficient sorting algorithms). When the median filter must be carried out in real time, the software implementation in general-purpose processorsdoes not usually give good results. This Paper presents an efficient algorithm for median filtering with a 3x3 filter kernel with only about 9 comparisons per pixel using spatial coherence between neighboring filter computations. The basic algorithm calculates two medians in one step and reuses sorted slices of three vertical neighboring pixels. An extension of this algorithm for 2D spatial coherence is also examined, which calculates four medians per step.
Highlights
Image processing is a very important field within industrial automation, and more concretely, in the automated visual inspection
The main challenge normally is the requirement of real-time results
One simple approach, which is often found in image processing textbooks, is to calculate the 3x3 median using a simple sorting algorithm, like bubble sort or quicksort, and pick the 5th element after the sorting
Summary
Image processing is a very important field within industrial automation, and more concretely, in the automated visual inspection In these applications, the main challenge normally is the requirement of real-time results. For example automatically analyzing predetermined f eatures of manufactured parts on an assembly line to look for defects and process variations Median filter is the nonlinear filter more used to remove the impulsive noise-from an image. The median filter is normally used to reduce noise in an image, somewhat like the mean filter. It often does a better job than the mean filter of preserving useful detail in the image
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