Removal of Canvas Patterns in Digital Acquisitions of Paintings

Cornelis, Bruno; Yang, Haizhao; Goodfriend, Alex; Ocon, Noelle; Lu, Jianfeng; Daubechies, Ingrid

doi:10.1109/tip.2016.2621413

Cited by 24 publications

(14 citation statements)

References 31 publications

(25 reference statements)

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“…Though adaptive time-series analysis is the main motivation of MIMF and MMD discussed in this paper, these models and algorithms can be naturally extended to higher dimensional spaces. Applications include atomic crystal images in physics [4,5], art investigation [8,9], geology [10,11,12], imaging [13], etc. In higher dimensional spaces, the computational efficiency is a crucial issue.…”

Section: Resultsmentioning

confidence: 99%

“…As shown in Figure 5, if f (t) is an ECG signal 2 , then it is more obvious to observe the change of the evolution pattern from R 0 (f )(t) than from f (t), e.g., the change of the height of R peaks and the width of QRS and S waves. In many applications [1,2,3,4,5,6,7,8,9,10,11,12,13], a signal would be a superposition of several MIMFs, for example, a complex signal…”

Section: (F )(T) =mentioning

confidence: 99%

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Multiresolution mode decomposition for adaptive time series analysis

Yang

2021

Applied and Computational Harmonic Analysis

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This paper proposes the multiresolution mode decomposition (MMD) as a novel model for adaptive time series analysis. The main conceptual innovation is the introduction of the multiresolution intrinsic mode function (MIMF) of the formto model nonlinear and non-stationary data with time-dependent amplitudes, frequencies, and waveforms. The multiresolution expansion coefficients {a n }, {b n }, and the shape function series {s cn (t)} and {s sn (t)} provide innovative features for adaptive time series analysis. For complex signals that are a superposition of several MIMFs with well-differentiated phase functions φ(t), a new recursive scheme based on Gauss-Seidel iteration and diffeomorphisms is proposed to identify these MIMFs, their multiresolution expansion coefficients, and shape function series. Numerical examples from synthetic data and natural phenomena are given to demonstrate the power of this new method.

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Section: Resultsmentioning

confidence: 99%

Section: (F )(T) =mentioning

confidence: 99%

Multiresolution mode decomposition for adaptive time series analysis

Yang

2021

Applied and Computational Harmonic Analysis

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“…Removing canvas patterns in fine art paintings can also be considered as a form of textured noise removal. For instance, the authors of [29] remove canvas patterns in paintings by fitting a parametric model of the canvas to the texture part given by the algorithm of [12].…”

Section: Related Workmentioning

confidence: 99%

A Non-Local Dual-Domain Approach to Cartoon and Texture Decomposition

Sur

2019

IEEE Trans. on Image Process.

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This paper addresses the problem of cartoon and texture decomposition. Microtextures being characterized by their power spectrum, we propose to extract cartoon and texture components from the information provided by the power spectrum of image patches. The contribution of texture to the spectrum of a patch is detected as statistically significant spectral components with respect to a null hypothesis modeling the power spectrum of a non-textured patch. The null-hypothesis model is built upon a coarse cartoon representation obtained by a basic yet fast filtering algorithm of the literature. Hence the term "dual domain": the coarse decomposition is obtained in the spatial domain and is an input of the proposed spectral approach. The statistical model is also built upon the power spectrum of patches with similar textures across the image. The proposed approach therefore falls within the family of non-local methods. Experimental results are shown in various application areas, including canvas pattern removal in fine arts painting, or periodic noise removal in remote sensing imaging.

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“…Based on this observation, we choose two dictionaries: one adapted to sparse encoding of wood grain texture, and a second to sparse encoding of painting-panel content. In the application of digital canvas removal [8], MCA was successfully used to decompose an image into a "cartoon" component and a "texture" component, based on its decomposition into the union of corresponding dictionaries. In our present application of extracting wood grain texture, we borrow the notions of cartoon and texture components, and likewise denote the wood-grain-like texture in I as I text , the cartoon like "paint blobs" as I cart p ; see Figure 12.…”

Section: Morphological Component Analysis (Mca)mentioning

confidence: 99%

“…Each spatial convolution operator Φ j,l is generated by the complex wavelet φ j,l . The DT-CWT is used as the dictionary for the image "content" in [8].…”

Section: Morphological Component Analysis (Mca)mentioning

confidence: 99%

Removing Cradle Artifacts in X-Ray Images of Paintings

Yin

Cornelis²,

Fodor³

et al. 2016

SIAM J. Imaging Sci.

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Abstract. We propose an algorithm that removes the visually unpleasant effects of cradling in X-ray images of panel paintings, with the goal of improving the X-ray image readability by art experts. The algorithm consists of three stages. In the first stage the location of the cradle is detected automatically and the grayscale inconsistency, caused by the thickness of the cradle, is corrected. In a second stage we use a method called morphological component analysis to separate the X-ray image into a so-called cartoon part and a texture part, where the latter contains mostly the wood grain from both the panel and the cradling. The algorithm next learns a Bayesian factor model that distinguishes between the texture patterns that originate from the cradle and those from other components such as the panel and/or the painting on the panel surface, and finally uses this to remove the textures associated with the cradle. We apply the algorithm to a number of historically important paintings on panel.We also show how it can be used to digitally remove stretcher artifacts from X-rays of paintings on canvas. We compare our results with those obtained manually by best current practices in art conservation as well as on a ground truth dataset, consisting of X-ray images of a painting before and after removal of the physically attached cradle.

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Removal of Canvas Patterns in Digital Acquisitions of Paintings

Cited by 24 publications

References 31 publications

Multiresolution mode decomposition for adaptive time series analysis

Multiresolution mode decomposition for adaptive time series analysis

A Non-Local Dual-Domain Approach to Cartoon and Texture Decomposition

Removing Cradle Artifacts in X-Ray Images of Paintings

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