Intrinsic Image Decomposition Using Multi-Scale Measurements and Sparsity

Automatic decomposition of intrinsic images, especially for complex real-world images, is a challenging under-constrained problem. Thus, we propose a new algorithm that generates and combines multi-scale properties of chromaticity differences and intensity contrast. The key observation is that the estimation of image reflectance, which is neither a pixel-based nor a region-based property, can be improved by using multi-scale measurements of image content. The new algorithm iteratively coarsens a graph reflecting the reflectance similarity between neighbouring pixels. Then multi-scale reflectance properties are aggregated so that the graph reflects the reflectance property at different scales. This is followed by a L₀ sparse regularization on the whole reflectance image, which enforces the variation in reflectance images to be high-frequency and sparse. We formulate this problem through energy minimization which can be solved efficiently within a few iterations. The effectiveness of the new algorithm is tested with the Massachusetts Institute of Technology (MIT) dataset, the Intrinsic Images in the Wild (IIW) dataset, and various natural images.