India is famous for its culture and civilization. Indian ancestors excelled in the field of Architecture, Art, Medicine, Astrology, etc., that are recognized and admired worldwide. They passed on the information about the heritage to their future generation in the form of sculptures, paintings and inscriptions. Paintings are one such art form which depicts the ancient history and culture. Chittannavasal and Tanjore paintings are very prominent in India which attracts people around the world. Tanjore painting play a significant role in Indian paintings which are made of vibrant colours, gold and precious stones. But due to climatic changes, lack of maintenance and rituals these paintings are degraded. Though many efforts are carried out to save these paintings from further damage, it is very challenging to restore the paintings from the existing degradation. However, with developing technology in the field of image processing has made this challenge achievable. These painting can be restored by formulating a degradation model and then developing an algorithm to restore the degraded portions. This paper deals with the various methodologies adopted for restoration of damaged images. This section describes the various restoration techniques adapted for paintings.
Marwa Jmal et al (2017) developed an image restoration with nonuniform illumination enhancement technique. Initially the authors performed contrast adjustment, then illumination is enhanced on the application of a modified homomorphic filter in the frequency domain. Optimal parameters were computed using golden section search algorithm to produce the enhanced image. At last, a color restoration function is applied to prevail over the problem of color violation. Their results yielded, local contrast improvement, detail enhancement, and preserving the originality of the image. The technique is applied on the collected dataset of cultural heritage images.
Jan Blazek et al (2009) have proposed a technology by combining the fresco art image of narrow-band ultraviolet, and the broad-band ultraviolet wavelength spectra. In addition, they used fusion of the old black and white photograph. They gathered all available information in such a way to view the details in one fused image using PCA transform. Then they performed the chemical analysis on the image spots using the spectroscopy and structure based neighborhood algorithm for the better visualization of the image.
Yuan Zeng and Yi Gong (2018) applied nearest neighbouring method for restoration of damaged ancient Chinese paintings that have tears, flakes and cracks. The damage detection was obtained by estimating a mask initially. Followed by the masking, the damaged part is constructed using Inpainting algorithm. The authors also discussed the application of deep learning algorithm as future research. Nikolaos Karianakis and Petros Maragos (2013) presented a computer vision system for robust restoration of prehistoric Tehran wall paintings. The authors applied an image stitching algorithm for image restoration. An area of relevant semantics, geometry and color in a different spot of the wall paintings was selected and stitched into the damaged area. Their key focus was the identification of damaged or missing area in the painting performed using morphological algorithm in addition with edge information. Ioannis Giakoumis et al (2006) presented a methodology for both detection and elimination of cracks on digitized paintings. Initially thresholding of the morphological top-hat transform was performed for crack identification. Then, median radial basis function is used to remove the misidentified cracks by region growing technique. Finally, crack filling using order statistics filters or controlled anisotropic diffusion is performed. The author claimed that their methodology was well suited for digitized paintings affected from cracks. Similar methodology was implemented by Shrinivas D Desai et al (2013) and they were able to achieved true positive rate of 98.3%.
M. Barni et al (2000) presented computer-guided and virtual artwork restoration techniques. These technique aids the restorer with virtual cleaning software to identify the best suitable cleaning procedure with a small patch of the paintings. Thus with initial study, it could be extended to the painting upon successful implementation with the small patch. Song Wei (2014) developed a novel framework based on hierarchy for the restoration of Chinese paintings. The framework involves 3 phases such as layering phase, hierarchy restoration phase and synthesis phase. In layering phase the painting was split into foreground and background layers. In hierarchy restoration phase, various image restoration algorithms were applied to these layers. In synthesis phase the restored image from the foreground and background were combined to get the complete restored painting. Ayman M. T. Ahmed (2009) introduced two different methodologies for color image restoration. The first technique involves blending of the standard deviation- weighted gray world and the Combined Gray World and Retinex (CGWR). The second technique was based on alteration of the Multi Scale Retinex (MSR) theory. In these techniques, the effect of neighboring pixels on the human eye is replicated for modifying the algorithms. In addition, the modified MSR is applied on CGWR technique to improve the performance of the basic algorithm. Their experimental results depicted the comparison between these two techniques with the basic traditional technique. Ioana Cortea et al (2020) presented analytical characterization of Romanian Monastery paintings. X-Ray Fluorescence (XRF) and Fourier Transform Infrared Spectroscopy (FTIR) were applied for the analysis. The data from FTIR supported XRF result to provide material characterization. The authors were able to identify many mineral pigments and the evidence of organic binders from the paintings.
From the literature studies it is evident that restoration of images plays a vital role in preserving the cultural heritage. This paper focuses on restoration of ancient Tanjore painting using image processing techniques in two ways one with segmentation process and the other with patch based inpainting technique.