Image Filtering by Reduced Kernels Exploiting Kernel Structure and Focal-Plane Averaging

Abstract—Incorporating multi-resolution capabilities into im-agers renders additional power saving mechanisms in the subse-quent image processing. In this paper, we show how, by exploiting a certain mask structure, 3 3 kernels can be reduced to 2 2 kernels if charge redistribution is provided at the focal plane of the imaging device. More precisely, by successively averaging two shifted half-resolution pixel grids, we will have a pre-processed image, subsampled by a factor of 2 on each dimension. It can also be extended to even lower resolution images if required. These pre-processed images can be filtered, for kernels matching the prescribed conditions, with a mask of a reduced size. Very useful image filtering kernels, like a 3 3 Gaussian kernel for image smoothing, or the well-known Sobel operators, fall into this category of reducible kernels. Operating onto the pre-processed image with one of these reduced kernels represents a smaller number of operations per pixel than realizing all the multiply-accumulate operations needed to apply a 3 3 kernel. Memory accesses are reduced in the same fraction. Concerning the difficulties of providing this pre-processed image representation, we propose a methodology for obtaining it at a very low power cost. It requires the implementation of user definable image subdivision and subsampling. Experimental results are given, obtained from measurements on a CMOS imager prototype chip incorporating these multi-resolution capabilities. I