In this paper, a fresh procedure to handle image mixtures by means of blind signal separation relying on a combination of second order and higher order statistics techniques are introduced. The problem of blind signal separation is reassigned to the wavelet domain. The key idea behind this method is that the image mixture can be decomposed into the sum of uncorrelated and/or independent sub-bands using wavelet transform. Initially, the observed image is pre-whitened in the space domain. Afterwards, an initial separation matrix is estimated from the second order statistics de-correlation model in the wavelet domain. Later, this matrix will be used as an initial separation matrix for the higher order statistics stage in order to find the best separation matrix. The suggested algorithm was tested using natural images.Experiments have confirmed that the use of the proposed process provides promising outcomes in identifying an image from noisy mixtures of images.
翻译:在本文中,引入了使用第二顺序和更高顺序统计技术组合的盲信号分离处理图像混合物的新程序。 盲信号分离问题被重新分配给波盘域。 这种方法背后的关键思想是, 图像混合物可以使用波盘变换, 分解成不相干和/ 或独立的子波段的总和。 最初, 观察到的图像在空间域中被预先抹黑。 随后, 从波盘域第二顺序统计脱热关系模型中估算出初步分离矩阵。 稍后, 这个矩阵将用作较高顺序统计阶段的初步分离矩阵, 以找到最佳的分离矩阵。 所建议的算法是用自然图像测试的。 实验证实, 使用拟议的过程在确定来自噪音图像混合物的图像方面提供了有希望的结果。