Fast spatial averaging: an efficient algorithm for 2D mean filtering
We present a new fast spatial averaging technique that efficiently implements operations for spatial averaging or two-dimensional mean filtering. To perform spatial averaging of an MxN image with an averaging filter of size mxn, our proposed method requires approximately 4MN additions and no division. This is very promising, since the major computations required by our algorithm depend only on the size of the original image but not on the size of the averaging filter. To our knowledge, this technique requires the smallest number of additions for mean filtering. Experimental results on various image sizes using different filter sizes confirm that our fast spatial averaging algorithm is significantly faster than other spatial averaging algorithms, especially when the size of the input image is very large.
spatial averaging mean filtering blurring complexity theory smoothing