Abstract:Blue noise is known for its uniformity in the spatial domain, avoiding the appearance of structures such as voids and clusters. Because of this characteristic, it has been adopted in a wide range of visual computing applications, such as image dithering, rendering and visualisation. This has motivated the development of a variety of generative methods for blue noise, with different trade-offs in terms of accuracy and computational performance. We propose a novel unsupervised learning approach that leverages a … Show more
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