Abstract
The standard method of programming kernel-based image processing tasks requires an exponential increase in processing time as kernel size increases. A new approach to programming kernel-based image processing tasks is presented that requires near constant time to process a given image at any given kernel size. A comparison is made using a standard method of average filtering and a new fast algorithm. Each algorithm was tested five times and processing times were averaged for kernel sizes ranging from 3×3 to 101×101. Results show the new algorithm is faster at all kernel sizes and orders of magnitude faster at larger kernel sizes (e.g., nearly a thousand times faster when using a 101×101 kernel size). A discussion is provided on other types of kernel-based image processing tasks that can also use this same programming approach.
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