Title: Robust Photometric Stereo Using Learned Image and Gradient Dictionaries,
Abstract: Photometric stereo is a method for estimating the normal vectors of an object
from images of the object under varying lighting conditions. Motivated by
several recent works that extend photometric stereo to more general objects and
lighting conditions, we study a new robust approach to photometric stereo that
utilizes dictionary learning. Specifically, we propose and analyze two
approaches to adaptive dictionary regularization for the photometric stereo
problem. First, we propose an image preprocessing step that utilizes an
adaptive dictionary learning model to remove noise and other non-idealities
from the image dataset before estimating the normal vectors. We also propose an
alternative model where we directly apply the adaptive dictionary
regularization to the normal vectors themselves during estimation. We study the
practical performance of both methods through extensive simulations, which
demonstrate the state-of-the-art performance of both methods in the presence of
noise.