Cosine integral images for fast spatial and range filtering

Non uniform kernels is important for many image processing algorithms. However, for large kernel sizes the filtering can become computationally expensive. We introduce cosine integral images (CII) which represent a large set of spatial and range filters, based on their frequency decomposition. The filtering requires a constant number of operations per image pixel, independent of filter size. We make use of CII to compute the Gabor filters, whose complexity is for the first time a constant O(l) operations per image pixel. We also improve previous constant time approximations of spatial Gaussian smoothing and bilateral filtering.