Blotch removal using multi-level scanning, shape analysis, and meta heuristic techniques

Valuable resources of artistic, historical, and cultural development of human life are stored in huge number of archives. These archives are suffering from a diversity of degradations and need to be restored. Blotches refer to the major degradations that mostly affect old films. In the current techniques of blotch detections, when high correct detection is required, the number of false alarms is high. Therefore, error in detection can cause some unnecessary changes in the uncorrupted pixels. On the other hand, due to restoration of blotches, fidelity may be affected and decreased because of the complex scene and large areas which are common in old archives. Thus, this research was aimed to enhance the performance of blotch detection comparing to the other available methods and to find a way to reconstruct blotches regardless of their sizes and scene complexity. In order to remove blotches from digitized old archives, two steps are necessary:detection of the position of blotches and restoration of the missing data. Regarding the detection, a post processing method based on a combination of pixel-based and objects-based methods was proposed. This post processing algorithm was provided based on a multi-level scanning and shape analysis which was presented for the better performance of high correct detection and lower false alarms for each given threshold. After identifying the position of blotches,reconstruction of missing data was the next step. Interpolation was organized based on just spatial information, and also spatial and temporal information. If the sizes of blotches are small, for example, less than 20 by 20 pixels, the process of reconstruction can be handled with traditional heuristic or previous model based methods, such as, Auto Regressive and Markov Random Field methods. Interpolation of the missing data for large area based on heuristic methods do not lead to a reasonable result, but the meta-heuristic techniques have the ability to remove small and large areas with better fidelity even in a scene with a complex background. Genetic algorithm and multi-layer back propagation neural network algorithm were adopted and consequently applied to a variety of benchmark samples of image sequences. These methods were proposed to find the missing data in a better way than the existing approaches. The final results were objectively and subjectively evaluated. Sign, Car, and Calendar image sequences were corrupted artificially with blotches of random size, shapes, and intensity. For objective assessment, in the field of detection of blotches, false alarms and correct detection were calculated and comprehensive comparisons were prepared based on Receiver Operation Characteristic. In addition, Mean Square Error, Normal Correlation, Image Enhancement Factor, and Peak Signal to Noise Ratio were calculated for restoration and the results were collected for comparison. The subjective evaluation also was done by requesting some respondent to judge the results. The algorithms were applied to two real image sequences which were contaminated to unknown blotches and the results were extracted for evaluation of proposed methods. Finally, a successful platform including blotch detection and correction was presented in this study, the proposed blotch removal approaches proves to have the potential to be applied to real blotches to restore old archives in real restoration process.

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