Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/44429
Title: Multiscale Tikhonov-Total Variation Image Restoration Using Spatially Varying Edge Coherence Exponent
Authors: Prasath, V. B. Surya 
Vorotnikov, Dmitry 
Pelapur, Rengarajan 
Jose, Shani 
Seetharaman, Guna 
Palaniappan, Kannappan 
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Project: info:eu-repo/grantAgreement/FCT/COMPETE/132981/PT 
Serial title, monograph or event: IEEE Transactions on Image Processing
Volume: 24
Issue: 12
Abstract: Edge preserving regularization using partial differential equation (PDE)-based methods although extensively studied and widely used for image restoration, still have limitations in adapting to local structures. We propose a spatially adaptive multiscale variable exponent-based anisotropic variational PDE method that overcomes current shortcomings, such as over smoothing and staircasing artifacts, while still retaining and enhancing edge structures across scale. Our innovative model automatically balances between Tikhonov and total variation (TV) regularization effects using scene content information by incorporating a spatially varying edge coherence exponent map constructed using the eigenvalues of the filtered structure tensor. The multiscale exponent model we develop leads to a novel restoration method that preserves edges better and provides selective denoising without generating artifacts for both additive and multiplicative noise models. Mathematical analysis of our proposed method in variable exponent space establishes the existence of a minimizer and its properties. The discretization method we use satisfies the maximum-minimum principle which guarantees that artificial edge regions are not created. Extensive experimental results using synthetic, and natural images indicate that the proposed multiscale Tikhonov-TV (MTTV) and dynamical MTTV methods perform better than many contemporary denoising algorithms in terms of several metrics, including signal-to-noise ratio improvement and structure preservation. Promising extensions to handle multiplicative noise models and multichannel imagery are also discussed.
URI: http://hdl.handle.net/10316/44429
Other Identifiers: 10.1109/TIP.2015.2479471
DOI: 10.1109/TIP.2015.2479471
Rights: openAccess
Appears in Collections:I&D CMUC - Artigos em Revistas Internacionais

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