Given a reference colour image and a destination gray scale image, this paper presents a novel automatic colourisation algorithm that transfers colour information from the reference image to the destination image. Since the reference and destination images may contain content at different or even varying scales (due to changes of distance between objects and the camera), existing texture matching based methods can often perform poorly. We propose a novel cross-scale texture matching method to improve the robustness and quality of the colourisation results. Suitable matching scales are considered locally, which are then fused using global optimisation that minimises both the matching errors and spatial change of scales. The minimisationis efficiently solved using a multi-label graph-cut algorithm.Since only low-level texture features are used, texture matching based colourisation can still produce semantically incorrect results, such as meadow appearing above the sky. We consider a class of semantic violation where the statistics of up-down relationships learnt from the reference image are violated and propose an effective method to identify and correct unreasonable colourisation. Finally, a novel non local `1 optimisation frame work is developed to propagate high confidence micro-scribbles to regions of lower confidence to produce a fully colourised image.Qualitative and quantitative evaluations show that our method outperforms several state-of-the-art methods.
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