Search results for "image restoration"
showing 10 items of 53 documents
Multi-Directional Detection of Scratches in Digitized Images
2009
Publication in the conference proceedings of EUSIPCO, Glasgow, Scotland, 2009
Explicit Algorithms for a New Time Dependent Model Based on Level Set Motion for Nonlinear Deblurring and Noise Removal
2000
In this paper we formulate a time dependent model to approximate the solution to the nonlinear total variation optimization problem for deblurring and noise removal introduced by Rudin and Osher [ Total variation based image restoration with free local constraints, in Proceedings IEEE Internat. Conf. Imag. Proc., IEEE Press, Piscataway, NJ, (1994), pp. 31--35] and Rudin, Osher, and Fatemi [ Phys. D, 60 (1992), pp. 259--268], respectively. Our model is based on level set motion whose steady state is quickly reached by means of an explicit procedure based on Roe's scheme [ J. Comput. Phys., 43 (1981), pp. 357--372], used in fluid dynamics. We show numerical evidence of the speed of resolution…
A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
1999
We present a new method for solving total variation (TV) minimization problems in image restoration. The main idea is to remove some of the singularity caused by the nondifferentiability of the quantity $|\nabla u|$ in the definition of the TV-norm before we apply a linearization technique such as Newton's method. This is accomplished by introducing an additional variable for the flux quantity appearing in the gradient of the objective function, which can be interpreted as the normal vector to the level sets of the image u. Our method can be viewed as a primal-dual method as proposed by Conn and Overton [ A Primal-Dual Interior Point Method for Minimizing a Sum of Euclidean Norms, preprint,…
A general framework for a class of non-linear approximations with applications to image restoration
2018
Este artículo se encuentra disponible en la página web de la revista en la siguiente URL: https://www.sciencedirect.com/science/article/abs/pii/S0377042717301188 Este es el pre-print del siguiente artículo: Candela, V., Falcó, A. & Romero, PD. (2018). A general framework for a class of non-linear approximations with applications to image restoration. Journal of Computational and Applied Mathematics, vol. 330 (mar.), pp. 982-994, que se ha publicado de forma definitiva en https://doi.org/10.1016/j.cam.2017.03.008 This is the pre-peer reviewed version of the following article: Candela, V., Falcó, A. & Romero, PD. (2018). A general framework for a class of non-linear approximations with applic…
A note on the Bregmanized Total Variation and dual forms
2009
This paper considers two approaches to perform image restoration while preserving the contrast. The first one is the Total Variation-based Bregman iterations while the second consists in the minimization of an energy that involves robust edge preserving regularization. We show that these two approaches can be derived form a common framework. This allows us to deduce new properties and to extend and generalize these two previous approaches.
Non Linear Image Restoration in Spatial Domain
2011
International audience; In the present work, a novel image restoration method from noisy data samples is presented. The restoration was per-formed by using some heuristic approach utilizing data samples and smoothness criteria in spatial domain. Unlike most existing techniques, this approach does not require prior modelling of either the image or noise statistics. The proposed method works in an interactive mode to find the best compromise between the data (mean square error) and the smoothing criteria. The method has been compared with the shrinkage approach, Wiener filter and Non Local Means algorithm as well. Experimental results showed that the proposed method gives better signal to noi…
Patch-Based Image Denoising Model for Mixed Gaussian Impulse Noise Using L1 Norm
2017
Image denoising is the classes of technique used to free the image form the noise. The noise in the image may be added during the observation process due to the improper setting of the camera lance, low-resolution camera, cheap, and low-quality sensors, etc. Noise in the image may also be added during the image restoration, image transmission through the transmission media. To obtain required information from image, image must be noise free, i.e., high-frequency details must be present in the image. There are number of applications where image denoising is needed such as remote location detection, computer vision, computer graphics, video surveillance, etc. In last two decades, numbers of m…
Art Painting Testing with Terahertz Pulse and Frequency Modulated Continuous Wave
2017
Paintings of individuals or collections undergo aging over time. The work of art restorers consists of repairing these defects using techniques that respect the history of the work. Ultraviolet, infrared and visible light and X-rays are well known techniques for analyzing these defects, but Terahertz is also increasingly used. Several works have shown that it is possible to detect hidden layers and various defects via terahertz pulses. In a previous work, we have shown that it is possible to use terahertz radiation to detect defects in the context of a restoration of a painting with a speed increase compared to time domain imaging.
HST observation of the inner coma of 2060 chiron
1995
Abstract The analysis of persistent comae of very large and distant comets allows us to infer the poorly known physical properties of dust and icy grains produced beyond Jupiter. Chiron is particularly interesting because Fulle ( Astron. Astrophys. 282 , 980–988; 1994) has shown that the strong asymmetricity of its coma (West, Astron. Astrophys. 241 , 635–645 1991) allows us to infer rotational properties of Chiron's nucleus. In this paper we report the observation of the asymmetric inner coma of Chiron from the analysis of 44 images of Chiron and eight images of a standard star available from the HST archive. The images were taken during February/March 1993, when Ch iron was 9.3 AU from th…
Total Variation Based Image Restoration
2004
For the purpose of image restoration the process of image formation can be modeled in a first approximation by the formula [207] $$ {u_d} = Q\{ II(k*u) + n\} , $$ (1.1) where u represents the photonic flux k is the point spread function of the optical-captor joint apparatus П is a sampling operator, i.e., a Dirac comb supported by the centers of the matrix of digital sensors, n represents a random perturbation due to photonic or electronic noise, and Qis a uniform quantization operator mapping ℝ to a discrete interval of values, typically [0, 255].