The variational approach to shape from shading
Computer Vision, Graphics, and Image Processing
A Method for Enforcing Integrability in Shape from Shading Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of digital image processing
Fundamentals of digital image processing
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Analytical Methods for Solving Poisson Equations in Computer Vision Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape from shading
A method for enforcing integrability in shape from shading algorithms
Shape from shading
A Theory of Photometric Stereo for a Class of Diffuse Non-Lambertian Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Existence and uniqueness in photometric stereo
Applied Mathematics and Computation
Structural Indexing: Efficient 3-D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Shape from shading with perspective projection
CVGIP: Image Understanding
Wavelet-based shape from shading
Graphical Models and Image Processing
Shape from shading and photometric stereo using surface approximation by Legendre polynomials
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination for computer generated pictures
Communications of the ACM
Robot Vision
Nonlinearities and Noise Reduction in 3-Source Photometric Stereo
Journal of Mathematical Imaging and Vision
The 2-D leap-frog: integrability, noise, and digitization
Digital and image geometry
Denoising images: non-linear leap-frog for shape and light-source recovery
Proceedings of the 11th international conference on Theoretical foundations of computer vision
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
A graph-spectral method for surface height recovery
Pattern Recognition
What is the range of surface reconstructions from a gradient field?
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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We present a gradient space technique for noise reduction in surfaces reconstructed from a noisy gradient field. We first analyze the error sources in the recovered gradient field of a surface using a three-image photometric stereo method. Based on this analysis, we propose an additive noise model to describe the errors in the surface gradient estimates. We then use a vector space formulation and construct a multiscale orthonormal expansion for gradient fields. Using the sparse representation properties of this expansion, we develop techniques for reducing the gradient field noise by coefficient selection with thresholding. The simulation results indicate that the proposed technique provides significant improvement on the noise levels of both the estimated gradient fields and the reconstructed surfaces under heavy noise levels. Furthermore, the experiments using noisy photometric stereo image triplets of real range data suggest that the additive model remains viable after the nonlinear photometric stereo operation to provide accurate noise removal.