Cellular Automata for Image Resizing

Abstract

Abstract. During the last years, several methods have been applied to tackle the image resizing problem. Most of these methods are derived from image interpolation techniques for image enlargement. Among them, the edge-directed interpolation methods succeed to preserve the edges of the low resolution image and produce crisper results compared to the space invariant models. In this chapter, we present an edge-directed method which exploits the simplicity and the inherent parallelism of the Cellular Automata (CA) computational tool to generate high resolution images from low resolution acquired images. This task is accomplished with the help of the Canny Edge Detector so as to discriminate the edge regions from the homogenous ones. Moreover, appropriate CA states and transition rules were designed to evolve the CA, which, eventually, attempt to enhance the quality of the edge areas. The orientation of the edge cells are considered in order to preserve effectively the edges of the initial image. The presented experimental results in terms of PSNR values and processing time demonstrate the effectiveness of the proposed method when compared to well-known methods as well as its suitability, especially for systems with low requirements specifications when further image processing is required.

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“…Lafe also proposed CA methods, by which information building blocks, called basis functions (or bases), can be generated from the evolving states, and called it Cellular Automata Transforms (CAT) applying them to image and video compression [30]. During the late years, it has also been shown by several researchers [31], [32], [33], [34], [35], [36], [37], [38] that CA can be used to perform some standard image processing tasks to a high level of performance, as well as in up-to-date computer vision fields, like stereo vision [39], [40], [41]. On the other hand, CAs have been extensively used as a VLSI architecture [42].…”