Abstract
Intrinsic images, which describe independent characteristics of scenes, are very useful in many different fields. But it is difficult to get them because the problem is extremely ill-posed. Smooth assumption is widely used in many methods, but pixels in plain areas and in edge areas are not well distinguished. We improve this assumption by adding a weight to every pixel in the image so that the smoothness is measured accordingly. There are always large error in dark areas in previous methods due to the enlarged inaccuracy there. We proposed a global assumption to solve this problem. The results show that the performance is greatly improved by using our method.
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Wang, Z., Zhu, L. (2012). Intrinsic Image Decomposition with Local Smooth Assumption and Global Color Assumption. In: Hu, SM., Martin, R.R. (eds) Computational Visual Media. CVM 2012. Lecture Notes in Computer Science, vol 7633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34263-9_4
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DOI: https://doi.org/10.1007/978-3-642-34263-9_4
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