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Found 7 papers, 1 papers with code
Image Denoising with Kernels based on Natural Image Relations
In this paper, we propose an alternative non-explicit way to take into account the relations among natural image wavelet coefficients for denoising: we use support vector regression (SVR) in the wavelet domain to enforce these relations in the estimated signal.
Iterative Gaussianization: from ICA to Random Rotations
The practical performance of RBIG is successfully illustrated in a number of multidimensional problems such as image synthesis, classification, denoising, and multi-information estimation.
Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis
Here we address the simultaneous statistical explanation of (i) the nonlinear behavior of achromatic and chromatic mechanisms in a fixed adaptation state, and (ii) the change of such behavior.
A Unified SVM Framework for Signal Estimation
On the one hand, the signal model equation is written in reproducing kernel Hilbert spaces (RKHS) using the well-known RKHS Signal Model formulation, and Mercer's kernels are readily used in SVM non-linear algorithms.
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
Hyperspectral images show similar statistical properties to natural grayscale or color photographic images.
On the Suitable Domain for SVM Training in Image Coding
We analytically demonstrate that no linear domain fulfills this condition because of the statistical and perceptual inter-coefficient relations that exist in these domains.
Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods
Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis.
