Search Results for author:
Found 8 papers, 2 papers with code
SPDGAN: A Generative Adversarial Network based on SPD Manifold Learning for Automatic Image Colorization
In this work, we propose a fully automatic colorization approach based on Symmetric Positive Definite (SPD) Manifold Learning with a generative adversarial network (SPDGAN) that improves the quality of the colorization results.
Patch-based image Super Resolution using generalized Gaussian mixture model
The minimum mean square error (MMSE) methodis a powerful image restoration method that uses a probability model on the patches of images.
Riemannian information gradient methods for the parameter estimation of ECD: Some applications in image processing
To develop the ISG method, the Riemannian information gradient is derived taking into account the product manifold associated to the underlying parameter space of the ECD.
PCA Reduced Gaussian Mixture Models with Applications in Superresolution
To learn the (low dimensional) parameters of the mixture model we propose an EM algorithm whose M-step requires the solution of constrained optimization problems.
Riemannian geometry for Compound Gaussian distributions: application to recursive change detection
A new Riemannian geometry for the Compound Gaussian distribution is proposed.
Generalized Shortest Path-based Superpixels for Accurate Segmentation of Spherical Images
Most of existing superpixel methods are designed to segment standard planar images as pre-processing for computer vision pipelines.
Texture Superpixel Clustering from Patch-based Nearest Neighbor Matching
In this paper, we propose a new Nearest Neighbor-based Superpixel Clustering (NNSC) method to generate texture-aware superpixels in a limited computational time compared to previous approaches.
Texture-Aware Superpixel Segmentation
Most superpixel algorithms compute a trade-off between spatial and color features at the pixel level.
