Image Completion from Low-level Learning
We present a learning-based approach to complete the missing parts of an image. Besides the conventional adopted image continuity and coherency heuristics, learnt image patches are used to better regularize the completion result. Through the learning process from a collection of commonly encountered natural images, we built a synthetic world consisting of scenes and their corresponding images. We further model the inter-patch relationships with a Markov Network. A belief propagation scheme is...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the Digital Imaging Computing: Techniques and Applications (DICTA 2005)|
|01_Zhu_Image_Completion_from_2005.pdf||523.65 kB||Adobe PDF||Request a copy|
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