IMAGE PROCESSING TECHNIQUES FOR THE DETECTION AND REMOVAL OF CRACKS IN DIGITIZED PAINTINGS

IMAGE PROCESSING TECHNIQUES FOR THE DETECTION AND REMOVAL OF CRACKS IN DIGITIZED PAINTINGS

 

Abstract

An integrated methodology for the detection and removal of cracks on digitized paintings is presented in this paper. The cracks are detected by thresholding the output of the morphological top-hat transform. Afterwards, the thin dark brush strokes which have been misidentified as cracks are removed using either a Median Radial Basis Function (MRBF) neural network on hue and saturation data or a semi-automatic procedure based on region growing. Finally, crack filling using order statistics filters or controlled anisotropic diffusion is performed. The methodology has been shown to perform very well on digitized paintings suffering from cracks.

 

 

CHAPTER ONE

INTRODUCTION

Many paintings, especially old ones, suffer from breaks in the substrate, the paint, or the varnish. These patterns are usually called cracks or craquelure and can be caused by aging, drying, and mechanical factors. Age cracks can result from non-uniform contraction in the canvas or wood-panel support of the painting, which stresses the layers of the painting. Drying cracks are usually caused by the evaporation of volatile paint components and the consequent shrinkage of the paint. Finally, mechanical cracks result from painting deformations due to external causes, e.g. vibrations and impacts.

The appearance of cracks on paintings deteriorates the perceived image quality. However, one can use digital image processing techniques to detect and eliminate the cracks on digitized paintings. Such a ”virtual” restoration can provide clues to art historians, museum curators and the general public on how the painting would look like in its initial state, i.e., without the cracks. Furthermore, it can be used as a non-destructive tool for the planning of the actual restoration. A system that is capable of tracking and interpolating cracks is presented in [1] . The user should manually select a point on each crack to be restored. A method for the detection of cracks using multi-oriented

November 30, 2005 DRAFT Gabor filters is presented in [2] . Crack detection and removal bears certain similarities with methods propose for the detection and removal of scratches and other artifacts from motion picture films [3] , [4] , [5] . However, such methods rely on information obtained over several adjacent frames for both artifact detection and filling and thus are not directly applicable in the case of painting cracks. Other research areas that are closely related to crack removal include image inpainting which deals with the reconstruction of missing or damaged image areas by filling-in information from the neighboring areas, and disocclusion, i.e., recovery of object parts that are hidden behind other objects within an image. Methods developed in these areas assume that the regions where information has to be filled-in are known. Different approaches for interpolating information in structured [6] , [7] , [8] , [9] , [10] and textured image areas [11] have been developed. The former are usually based on partial differential equations (PDE) and on the calculus of variations whereas the latter rely on texture synthesis principles. A technique that decomposes the image to textured and structured areas and uses appropriate interpolation techniques depending on theareawhere the missing information lies has also been proposed [12] . The results obtained by these techniques are very good. A methodology for the restoration of cracks on digitized paintings, which adapts and integrates a number of image processing and analysis tools is proposed in this paper. The methodology is an extension of the crack removal framework presented in [13] . The technique consists of the following stages:

• Crack detection.
• Separation of the thin dark brush strokes, which have been misidentified as cracks.
• Crack filling (interpolation).

A certain degree of user interaction, most notably in the crack detection stage, is required for optimal results. User interaction is rather unavoidable since the large variations observed in the typology of cracks would lead any fully automatic algorithm to failure. However, all processing steps can be executed in real time and thus the user can instantly observe the effect of parameter tuning on the image under study and select in an intuitive way the values that achieve the optimal visual result. Needless to say that only subjective optimality criteria can be used in this case since no ground truth data are available. The opinion of restoration experts that inspected the virtually restored images was very positive.

Digital image processing techniques can be used in the restoration of digitized paintings which contains cracks. Old Paintings suffer from breaks in the paint, or the varnish. These patterns are usually called cracks and can be caused by aging, drying, and other mechanical factors. This cracks can be rectangular, circular, spider-web, unidirectional, tree branches and random. Cracks also depend on the materials used for the painting, the painting technique of the artist, the atmospheric variations and storage conditions. The appearance of cracks on paintings deteriorates the legibility of image.

A virtual restoration can provide clues to art historians, museum curators and the general public on how the painting would look like in its initial state.

Problem Statement:

Among many proposed system crack were detected manually where user needs to specify the initial crack location for filling of cracks. The Crack filling algorithm gives the blurring effect on image.

Proposed system will detect crack automatically. It will find low luminance intensity point and apply top hat transformation. And then fill these crack pixels using order statistics filter.

Motivation:

Paintings which are made by paint or other material suffer from break due to some factor; it makes some pattern like structure is called cracks. Because of this cracks paintings are damaged. Original effect of painting is lost. Craquelure is the fine pattern of dense "cracking" formed on the surface of materials, either as part of the process of ageing or of their original formation or production. The term is most often used to refer to tempera or oil paintings, where it is a sign of age that is also sometimes induced in forgeries and ceramics. It can also develop in old ivory carvings, and painted miniatures on an ivory backing are prone to craquelure.

So in our project we are trying to restore paintings virtually so that people can get idea of that painting i.e. how the paintings usually look like.Our proposed system is automated to detect and remove the cracks from the cracked images.It can be used in museums in case of expensive paintings.

Scope

Old cracked painting will be scanned and converted to digital image by converting it to jpeg or jpg format.Proposed system will detect only low luminance pixel by Top Hat Transform and it will not detect high intensity pixel values.The Top Hat transformation generates a grayscale output image with no background. Threshold is applied to generate crack map. Crack Filling is done by order statistic Filters.

 

 

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