Image variational denoising using gradient fidelity on curvelet shrinkage

Authors:
Liang Xiao;Li-Li Huang;Badrinath Roysam
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China and Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Inst ...;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China and Department of Information and Computing Science, Guangxi University of Technology, Liuzh ...;Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on robust processing of nonstationary signals
Year:
2010

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Abstract

A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then, we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture, the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts, while preserving fine details.