Application of Convolutional Neural Network (CNN) and different other techniques for the restoration of degraded folk artworks: a comparative performance analysis

Folk art is an important manifestation of culture and heritage of a region. However, as these artworks are not properly cared for, so degrades very fast and lost permanently over the time. Restoration and proper preservation of these kind of artworks are very much essential. Neural networks have emerged as powerful tools in image processing, overpowering the classical methods, especially in pattern recognition. Convolutional Neural Network (CNN) is among the most utilised deep neural networks. In machine learning issues, the CNN performs admirably. In this paper, a comparative performance analysis of CNN and its combination with BBHE (Brightness preserving Bi-Histogram Equalization), fuzzy technique and PSO (Particle Swarm Optimization) is presented. The performance has been measured on the basis of CPP (Contrast per Pixel), AMBE (Absolute Mean Brightness Error), NMSE (Normalized Mean Square Error), IEF (Image Enhancement Factor), PSNR (Peak to Signal Noise Ratio), r (Pearson Correlation Coefficient) and SSIM (Structural Similarity Index).