piecewise-constant smoothing is revisited in this paper. Starting from its generalized formulation, we propose a numerical scheme/framework for solving it via a series of weighted-average filtering (e.g., box filtering, Gaussian fil-tering, bilateral filtering, and guided filtering). Because of the equivalence between M-smoother and local-histogram-based filters (such as median filter and mode filter), the proposed framework enables fast approximation of histogram filters via a number of box filtering or Gaus-sian filtering. In addition, high-quality piecewise-constant smoothing can be achieved via a number of bilateral filtering or guided filtering integrated in the proposed framework. Experiments on depth map denoising show the effectiven...
Edge-aware smoothing is an essential tool for computer vision, graphics and photography. In this pap...
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer S...
In this paper, we propose a semi-sparsity smoothing method based on a new sparsity-induced minimizat...
Abstract—We present a generalization of the bilateral filter that can be applied to feature-preservi...
In this paper, we present a new and efficient method to implement robust smoothing of low-level sign...
In this paper, we present a new and efficient method to implement robust smoothing of low-level sign...
In the field of image analysis, denoising is an important preprocessing task. The design of an effic...
Removing noise from signals which are piecewise constant (PWC) is a challenging signal processing pr...
In this thesis, a local smoothing method, termed the not-so-smoother, designed to estimate disconti...
Removing noise from signals which are piecewise constant (PWC) is a challenging signal processing pr...
In the field of medical image analysis, denoising is one of the most important preprocessing steps b...
Recently, two piecewise smooth models L0smoothing and relative total variation (RTV) have been propo...
Local linear M-smoothing is proposed as a method for image processing. It is more appropriate than c...
First we explain the interplay between robust loss functions, nonlinear filters and Bayes smoothers ...
This paper presents three novel methods that enable bilateral filtering in constant time O(1) withou...
Edge-aware smoothing is an essential tool for computer vision, graphics and photography. In this pap...
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer S...
In this paper, we propose a semi-sparsity smoothing method based on a new sparsity-induced minimizat...
Abstract—We present a generalization of the bilateral filter that can be applied to feature-preservi...
In this paper, we present a new and efficient method to implement robust smoothing of low-level sign...
In this paper, we present a new and efficient method to implement robust smoothing of low-level sign...
In the field of image analysis, denoising is an important preprocessing task. The design of an effic...
Removing noise from signals which are piecewise constant (PWC) is a challenging signal processing pr...
In this thesis, a local smoothing method, termed the not-so-smoother, designed to estimate disconti...
Removing noise from signals which are piecewise constant (PWC) is a challenging signal processing pr...
In the field of medical image analysis, denoising is one of the most important preprocessing steps b...
Recently, two piecewise smooth models L0smoothing and relative total variation (RTV) have been propo...
Local linear M-smoothing is proposed as a method for image processing. It is more appropriate than c...
First we explain the interplay between robust loss functions, nonlinear filters and Bayes smoothers ...
This paper presents three novel methods that enable bilateral filtering in constant time O(1) withou...
Edge-aware smoothing is an essential tool for computer vision, graphics and photography. In this pap...
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer S...
In this paper, we propose a semi-sparsity smoothing method based on a new sparsity-induced minimizat...