Shadow removal is an important problem for both artists and algorithms. Previous methods handle some shadows well but, because they rely on the shadowed data, perform poorly in cases with severe degradation. Image-completion algorithms can completely replace severely degraded shadowed regions, and perform well with smaller-scale textures, but often fail to reproduce larger-scale macrostructure that may still be visible in the shadowed region. This paper provides a general framework that leverages degraded (e.g., shadowed) data to guide the image completion process by extending the objective function commonly used in current state-of-the-art image completion energy-minimization methods. This approach achieves realistic shadow removal even in cases of severe degradation and could be extended to other types of localized degradation.
College and Department
Physical and Mathematical Sciences; Computer Science
BYU ScholarsArchive Citation
Hintze, Ryan Sears, "Shadow Patching: Exemplar-Based Shadow Removal" (2017). All Theses and Dissertations. 6664.
Computer vision, shadow removal, image completion, PatchMatch