A Practical Approach for Simultaneous Estimation of Light Source Position, Scene Structure, and Blind Restoration Using Photometric Observations

Abstract

Given blurred observations of a stationary scene captured using a static camera but with different and unknown light source positions, we estimate the light source positions and scene structure (surface gradients) and perform blind image restoration. The images are restored using the estimated light source positions, surface gradients, and albedo. The surface of the object is assumed to be Lambertian. We first propose a simple approach to obtain a rough estimate of the light source position from a single image using the shading information which does not use any calibration or initialization. We model the prior information for the scene structure as a separate Markov random field (MRF) with discontinuity preservation, and the blur function is modeled as Gaussian. A proper regularization approach is then used to estimate the light source position, scene structure, and blur parameter. The optimization is carried out using the graph cuts approach. The advantage of the proposed approach is that its time complexity is much less as compared to other approaches that use global optimization techniques such as simulated annealing. Reducing the time complexity is crucial in many of the practical vision problems. Results of experimentation on both synthetic and real images are presented.