Paintings and their frames are made of many different materials. These include varnish, paint, glue, canvas, wood, metal, gilding and plaster. Together they form a complex structure that is easily damaged if knocked or dropped. The materials are also sensitive to, and can be damaged by, the surrounding environment, particularly extremes changes in humidity and heat, as well as by light and dirt. 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 nonuniform 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 nondestructive tool for the planning of the actual restoration.
    In this design, a patch based approach has been employed for the removal of cracks from digitized paintings. For this, we have to perform some image operations or the filters to resolve the problem. We are expected to use some model to handle each stage individually. Under the lifecycle of Image processing, the image is already acquired and we are just presenting the raw paint image. Next to detect the Cracks we have to process. A crack can be the type of edge that is abnormally dark and highlighted then the other image areas. It gives the look as of the edge, we are trying to implement the edge based filtering tools to get the enhanced image the expected algorithm to detect the crack or to perform edge detection.
The design approach of the algorithm is given below,
1. Find all boundary point of the cracked portion
2. Find data pattern for each boundary point
3. Multiply data pattern & confident of each Boundary point (It is total point/strength/priority of particular pixel)
4. Find the pixel on boundary point which one having the highest priority
5. Create a patch window by keeping that highest priority pixel as the central one
6. Match this window with the entire image from (0,0)th pixel to (x,y)th pixel and identify the matched window.
7. Collect all the matched window position and again find data pattern for each matched window, and multiply data pattern.
8. Find the highest priority matched window then replace the patch window, which has been created in point number (5), with currently got matched window
9. Repeat the above steps unless there is no further inpainting needed.

Reference Paper: Inpainting Approach to Repair Cracked Images
Author’s Name:  Shilpa and Nisha Sharma
Source: IJAIEM

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