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Super-resolution reconstruction of image is highly dependent on the data outliers. This work addresses the super-resolution reconstruction design of the intersecting cortical model (ICM) algorithm applied to the cubic spline interpolation. Based on a simplification of the pulse-coupled neural network (PCNN), we propose a design strategy to reduce the effects of outliers on the reconstructed image. Intersecting cortical model (ICM) has gained widely research as a new artificial neural network. It derives directly from the studies of the small mammal's visual cortex. The theory analysis and the simulation experiments of the image processing indicate that this kind of super-resolution reconstruction algorithm can not only reduce the effects of outliers effectively but also keep the details of the image sufficiently.