Image sequence decomposition via sigma-delta cellular neural network having coupled cells

Abstract

In this paper, an image sequence decomposition by the sigma-delta cellular neural network (SD-CNN) with coupled cells and its composition framework is proposed. The SD-CNN, having coupled cells inspired by the second-order sigma-delta modulator, is employed for image sequence decomposition, and it enables effective image decomposition by the effects of complex dynamics. In our method, pixel luminance is described by the number of spikes within a discrete time window that corresponds to the number of SD-CNN iterations. For efficient image decomposition, a high luminance which requires a long time window, should be compensated. To solve this problem, we introduce the cumulated luminance integrator enables compensating a high luminance even with a short time window. Experimental results on various test images in the Kodak dataset and the Waterloo dataset support that all the test images can be mathematically lossless reconstructed from an image sequence decomposed via our method. It is also confirmed that the proposed method improves transmission efficiency for the Kodak dataset by approximately 80%, which is an issue with our previous method.