Abstract
A mural is any piece of artwork sculpted or applied directly on a wall, ceiling or other permanent surface. This artwork symbolizes various culture’s, traditions, historical events, spiritual stories, and civilizations of respective societies of ancient times. But these mural paintings are subjected to degradation either by various natural causes as well as pollution or by human beings without knowing their value. Restoring these paintings requires skilled artisans who are hard to find these days. Consequently, an efficient image restoration technique is required to meet the particular needs of the paintings. Existing in-painting algorithms largely use pixel-based textural reconstruction. The technique, however, does not work well for images with large, degraded portions. and also fails in the restoration of the structure. To resolve these drawbacks, we propose a combined technique for the textural and structural reconstruction of ancient murals. The proposed Extended Exemplar-based Region-Filling Algorithm uses a patch-based reconstruction procedure and masked images are created automatically using the Dynamic Mask Generation Algorithm. The deteriorated portions are identified by creating masks, and masks are created in such a way that degraded portions have a pixel intensity value of one and the remaining part has a value of zero, and filling is done by analyzing the surrounding pixel values of the degraded pixel. The algorithm reconstructs the structure of the paintings efficiently by generating sketches. The proposed technique reconstructs both the structure and textural information, and ensures efficient reconstructed results, compared to existing in-painting techniques. Performance is evaluated by metrics such as the Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM).
Highlights
A mural painting [1] is any bit of work of art painted or sculpted straightforwardly on a wall, roof, or other perpetual surface
This algorithm succeeded in filling the degraded regions seen in ancient temple murals without any explicit and implicit segmentation
This paper proposes an automated system for the identification and reconstruction of degraded parts seen in the ancient temple murals
Summary
A mural painting [1] is any bit of work of art painted or sculpted straightforwardly on a wall, roof, or other perpetual surface. Ancient temple murals symbolize various cultures, traditions, and civilizations of respective societies of ancient. The artists of Tamil Virtual Academy, a part of the Tamil Nadu Archaeology Department, manually reconstructs the deteriorated temple murals by using paint and brush. This is a time-consuming task and the availability of skilled artisans reduced while the demand for reconstruction rose. Digital inpainting has become popular with the improvement of image processing tools. The capability of these processing tools led to the availability of different methods for image processing. Image inpainting [2] has become a challenging topic of research in image processing [3] and computer vision
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