Image filtering by reduced kernels exploiting kernel structure and focal-plane averaging

Abstract

Incorporating multi-resolution capabilities into imagers renders additional power saving mechanisms in the subsequent 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 averaging and shifting a half-resolution pixel grid, we will have a pre-processed image, subsampled by a factor of 2 on each dimension, that can be filtered 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 at the focal plane. Experimental results are given, obtained from measurements on a CMOS imager prototype chip incorporating these multi-resolution capabilities.