Skip to Main Content
This paper presents a computationally efficient algorithm for smoothly space-variant Gaussian blurring of images. The proposed algorithm uses a specialized filter bank with optimal filters computed through principal component analysis. This filter bank approximates perfect space-variant Gaussian blurring to arbitrarily high accuracy and at greatly reduced computational cost compared to the brute force approach of employing a separate low-pass filter at each image location. This is particularly important for spatially variant image processing such as foveated coding. Experimental results show that the proposed algorithm provides typically 10 to 15 dB better approximation of perfect Gaussian blurring than the blended Gaussian pyramid blurring approach when using a bank of just eight filters.