Search Results for author:
Found 7 papers, 0 papers with code
Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models
Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements.
Panoramic Robust PCA for Foreground-Background Separation on Noisy, Free-Motion Camera Video
We perform extensive numerical experiments on both static and moving camera video subject to a variety of dense and sparse corruptions.
Robust Photometric Stereo via Dictionary Learning
We propose a model that applies dictionary learning to regularize and reconstruct the normal vectors from the images under the classic Lambertian reflectance model.
Robust Surface Reconstruction from Gradients via Adaptive Dictionary Regularization
This paper introduces a novel approach to robust surface reconstruction from photometric stereo normal vector maps that is particularly well-suited for reconstructing surfaces from noisy gradients.
Robust Photometric Stereo Using Learned Image and Gradient Dictionaries
Photometric stereo is a method for estimating the normal vectors of an object from images of the object under varying lighting conditions.
Augmented Robust PCA For Foreground-Background Separation on Noisy, Moving Camera Video
This work presents a novel approach for robust PCA with total variation regularization for foreground-background separation and denoising on noisy, moving camera video.
Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging
For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements.
