Image Zooming Algorithms Based on Granular Computing with l∞-norm

摘要

The granular computing with l_(∞)-norm is used to zoom the image. Firstly, a granule is represented by l_(∞)-norm and has the form of hypercube. Secondly, the bottle-up computing model is adopted to transform the microcosmic world into the macroscopic world by the designed join operation between two hypercube granules. The proposed granular computing is used to zoom the image and achieves the super-resolution image for the input low-resolution image. Experimental results show that the granular computing with l_(∞)-norm reduces the error between the original image and the reconstructed super-resolution image compared with bicubic interpolation and sparse representation.