An adaptive confidence measure for optical flows based on linear subspace projections
Proceedings of the 29th DAGM conference on Pattern recognition
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In this paper we combine the concept of adaptive filters with neural networks in order to be able to include high level knowledge about the contents of the image in the filtering process. Adaptive image enhancement algorithms often utilize low level knowledge like gradient information to guide filtering parameters. The advantage is that these filters do not need any specific knowledge and can thus be applied to a broad spectrum of images. However, for many problems this low level information is not sufficient to achieve good results. For example in medical imaging it is often very important that some features are preserved while others are suppressed. Usually these features cannot be distinguished by low level information. Therefore we propose a method to incorporate high level knowledge in the filtering process in order to adjust the parameters of any given filter thus creating a guided filter. We present a scheme for acquiring this high level knowledge which allows us to apply our method to all kinds of images using pattern recognition and special preprocessing techniques. The design of the guided filter itself is easy as for the high level knowledge only some sample pixels including their neighborhood and the desired parameters for these pixels are necessary.