Abstract
In this paper, we proposed a bottom-up computational model of visual filling-in to recover not only the texture but also the structure pattern in the unknown area of the images. Different from previous works of image inpainting and texture synthesis, our approach in the first step recovers the structure information of the missing part of an image; and then in the second step, each missing region with homogeneous composition is recovered independently. The structure recovery strategy is based on Gestalt laws of human visual perception, especially the good continuation law that predict the curvilinear continuity in contour completion of human behavior. In the experiment section, we provide the comparative results of our model and other proposed methods. Our model can achieve better performance in recovering images, especially when the scene contains rich structural information.
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Ma, J. (2009). Image Filling-In: A Gestalt Approach. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_86
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DOI: https://doi.org/10.1007/978-3-642-02490-0_86
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