Hybrid Noise for LIC-Based Pencil Hatching Simulation

Line Integral Convolution (LIC) has been widely adopted in pencil hatching generation, where an image degraded by random binary white noise (RBWN) is filtered by LIC along a priori vector field. Nonetheless, an RBWN degraded image through LIC produces hatching graduation only in terms of stroke intensity, and can neither produce hatching graduation explicitly in stroke density nor create visually clear hatching strokes while input pixel values are fairly low. In this paper we address these issues from a noise point of view by assessing several noise models and subsequently constructing a new noise model, called hybrid noise. The new noise model has been experimentally demonstrated in simulating hatching graduation in terms of both stroke intensity and stroke density with quantified graduality measurements. To oversee the effectiveness of hybrid noise we implement the whole pipeline of pencil drawing simulation and compare our results with those state of the art algorithms.