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Photometric stereo is a method for estimating the normal vectors of an objectfrom images of the object under varying lighting conditions. Motivated byseveral recent works that extend photometric stereo to more general objects andlighting conditions, we study a new robust approach to photometric stereo thatutilizes dictionary learning. Specifically, we propose and analyze twoapproaches to adaptive dictionary regularization for the photometric stereoproblem. First, we propose an image preprocessing step that utilizes anadaptive dictionary learning model to remove noise and other non-idealitiesfrom the image dataset before estimating the normal vectors. We also propose analternative model where we directly apply the adaptive dictionaryregularization to the normal vectors themselves during estimation. We study thepractical performance of both methods through extensive simulations, whichdemonstrate the state-of-the-art performance of both methods in the presence ofnoise.

Robust Photometric Stereo Using Learned Image and Gradient Dictionaries