Modified soft-decision adaptive interpolation by an evolutionary game

Soft-decision adaptive interpolation (SAI) provides a powerful result in preserving edge structures for interpolation from low-resolution images to obtain high-resolution images. However, the SAI algorithm may produce artifacts in the smooth regions and texture patterns of the interpolated images. To improve the SAI algorithm, we propose an evolutionary game, where every pixel is regarded as a player. The players are divided into two roles, one for unknown high-resolution pixels and the other for known low-resolution pixels. Each role has a different strategy set. The evolutionarily stable strategy in each local image region is a mixed strategy adopted by every player. By considering the mixed strategy as weights for interpolation, we can adaptively estimate the high-resolution image. Experimental results show that the proposed algorithm improves the SAI algorithm by alleviating the artifacts both in PSNR and visual quality.