Image Processing Techniques for Cracks Detection and Removal

Abstract

An integrated methodology for the detection and removal of cracks on digitized paintings is presented. 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. In some paintings, certain areas exist where brush strokes have almost the same thickness and luminance features as cracks. Cracks usually have low luminance and thus can be considered as local intensity minima with rather elongated structural characteristics. Therefore, a crack detector can be applied on the luminance component of an image and should be able to identify such minima. A crack detection procedure based on the so-called top-hat transform is proposed. The top-hat transform generates a grayscale output image where pixels with a large grey value are potential crack or crack-like elements. Therefore, a thresholding operation is required to separate cracks from the rest of the image. The threshold value can be chosen by a trial and error procedure. The low computational complexity of the thresholding operation enables the user to view the crack detection results in real time while changing the threshold value. This fact makes interactive threshold selection very effective and intuitive. Alternatively, threshold selection can be done by inspecting the histogram to be close to the maximum intensity value, and assigning it a value that separates this lobe from the rest of the intensities. Instead of this global thersholding technique, more complex thresholding schemes, which use a spatially varying threshold, can be used. it is more preferable to select the threshold so that some cracks remain undetected than to choose a threshold that would result in the detection of all cracks but will also falsely identify as cracks, and subsequently modify, other image structures.

Existing System

 The existing methods for processing digital images are there which actually deal with enhancing the image picture quality, brightness, color etc.  These factors can be degraded due to aging process.  Such a image processing technique algorithm concentrates on improving those factor alone. There are not designed to analysis and improve in the cracks region.  The cracks removal has to be rectified in the different manner.  The principle applied to improve image color, brightness and other characteristic can not be used for crack detection and removal.  This project concentrates on the digital image processing algorithm that deals only with crack detection and removal.

Proposed System

 The former are usually based on partial differential equations 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 the area where the missing information lies has also been proposed.  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 technique consists of the following stages: 1. Crack detection. 2. Separation of the thin dark brush strokes, which have been misidentified as cracks. 3. Crack filling (interpolation)..  User interaction is rather unavoidable since the large variations observed in the typology of cracks would lead any fully automatic algorithm to failure.

Architecture