Use of medial axis transforms for computing normals at boundary points

Medial axis transform (MAT) is well known for object representation. It is interesting to explore its use in different kinds of computations. In this paper an algorithm has been proposed for computation of normals at the boundaries of two-dimensional objects based on their MATs. In this technique, there is no requirement of linking boundary points during the computation compared to other existing techniques. The added advantage in the computation is that the computation can be restricted purely in the integer domain.     

Adaptive total variation denoising based on difference curvature

Image denoising methods based on gradient dependent regularizers such as Rudin et al.’s total variation (TV) model often suffer the staircase effect and the loss of fine details. In order to overcome such drawbacks, this paper presents an adaptive total variation method based on a new edge indicator, named difference curvature, which can effectively distinguish between edges and ramps. With adaptive regularization and fidelity terms, the new model has the following properties: at object edges, the regularization term is approximate to the TV norm in order to preserve the edges, and the weight of the fidelity term is large in order to preserve details; in flat and ramp regions, the regularization term is approximate to the L2 norm in order to avoid the staircase effect, and the weight of the fidelity term is small in order to strongly remove the noise. Comparative results on both synthetic and natural images demonstrate that the new method can avoid the staircase effect and better preserve fine details.     

Computation of medial axis and offset curves of curved boundaries in planar domain

In this paper, we begin our research from the generating theory of the medial axis. The normal equidistant mapping relationships between two boundaries and its medial axis have been proposed based on the moving Frenet frames and Cesaro’s approach of the differential geometry. Two pairs of adjoint curves have been formed and the geometrical model of the medial axis transform of the planar domains with curved boundaries has been established. The relations of position mapping, scale transform and differential invariants between the curved boundaries and the medial axis have been investigated. Based on this model, a tracing algorithm for the computation of the medial axis has been generated. In order to get the accurate medial axis and branch points, a Two_Tangent_Points_Circle algorithm and a Three_Tangent_Points_Circle algorithm have been generated, which use the results of the tracing algorithm as the initial values to make the iterative process effective. These algorithms can be used for the computation of the medial axis effectively and accurately. Based on the medial axis transform and the envelope theory, the trimmed offset curves of curved boundaries have been investigated. Several numerical examples are given at the end of the paper.     

Total variation and high-order total variation adaptive model for restoring blurred images with Cauchy noise

In this paper, we propose a novel model to restore an image corrupted by blur and Cauchy noise. The model is composed of a data fidelity term and two regularization terms including total variation and high-order total variation. Total variation provides well-preserved edge features, but suffers from staircase effects in smooth regions, whereas high-order total variation can alleviate staircase effects. Moreover, we introduce a strategy for adaptively selecting regularization parameters. We develop an efficient alternating minimization algorithm for solving the proposed model. Numerical examples suggest that the proposed method has the advantages of better preserving edges and reducing staircase effects.     

Some First-Order Algorithms for Total Variation Based Image Restoration

This paper deals with first-order numerical schemes for image restoration. These schemes rely on a duality-based algorithm proposed in 1979 by Bermùdez and Moreno. This is an old and forgotten algorithm that is revealed wider than recent schemes (such as the Chambolle projection algorithm) and able to improve contemporary schemes. Total variation regularization and smoothed total variation regularization are investigated. Algorithms are presented for such regularizations in image restoration. We prove the convergence of all the proposed schemes. We illustrate our study with numerous numerical examples. We make some comparisons with a class of efficient algorithms (proved to be optimal among first-order numerical schemes) recently introduced by Y. Nesterov.     

Medial axis computation for planar free-form shapes

We present a simple, efficient, and stable method for computing—with any desired precision—the medial axis of simply connected planar domains. The domain boundaries are assumed to be given as polynomial spline curves. Our approach combines known results from the field of geometric approximation theory with a new algorithm from the field of computational geometry. Challenging steps are (1) the approximation of the boundary spline such that the medial axis is geometrically stable, and (2) the efficient decomposition of the domain into base cases where the medial axis can be computed directly and exactly. We solve these problems via spiral biarc approximation and a randomized divide & conquer algorithm.     

Spatially dependent regularization parameter selection in total generalized variation models for image restoration

In this paper, the automated spatially dependent regularization parameter selection framework for multi-scale image restoration is applied to total generalized variation (TGV) of order 2. Well-posedness of the underlying continuous models is discussed and an algorithm for the numerical solution is developed. Experiments confirm that due to the spatially adapted regularization parameter, the method allows for a faithful and simultaneous recovery of fine structures and smooth regions in images. Moreover, because of the TGV regularization term, the adverse staircasing effect, which is a well-known drawback of the total variation regularization, is avoided.     

Total variation, adaptive total variation and nonconvex smoothly clipped absolute deviation penalty for denoising blocky images

The total variation-based image denoising model has been generalized and extended in numerous ways, improving its performance in different contexts. We propose a new penalty function motivated by the recent progress in the statistical literature on high-dimensional variable selection. Using a particular instantiation of the majorization-minimization algorithm, the optimization problem can be efficiently solved and the computational procedure realized is similar to the spatially adaptive total variation model. Our two-pixel image model shows theoretically that the new penalty function solves the bias problem inherent in the total variation model. The superior performance of the new penalty function is demonstrated through several experiments. Our investigation is limited to “blocky” images which have small total variation.     

A Truncated Lagrange Method for Total Variation-Based Image Restoration

In the last years, Total Variation minimization has become a popular and valuable technique for the restoration of noisy and blurred images. In this paper, we present a new technique for image restoration based on Total Variation minimization and the discrepancy principle. The new approach replaces the original image restoration problem with an equality constrained minimization problem. An inexact Newton method is applied to the first-order conditions of the constrained problem. The stopping criterium is derived from the discrepancy principle. Numerical results of image denoising and image deblurring test problems are presented to illustrate the effectiveness of the new approach.