De-Noising of Binary Image Using Accelerated Local Median-Filtering Approach

De-Noising of Binary Image Using Accelerated Local Median-Filtering Approach

Amit Khan (RCC Institute of Information Technology, India) and Dipankar Majumdar (RCC Institute of Information Technology, India)
DOI: 10.4018/978-1-5225-7107-0.ch007
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In the last few decades huge amounts and diversified work has been witnessed in the domain of de-noising of binary images through the evolution of the classical techniques. These principally include analytical techniques and approaches. Although the scheme was working well, the principal drawback of these classical and analytical techniques are that the information regarding the noise characteristics is essential beforehand. In addition to that, time complexity of analytical works amounts to beyond practical applicability. Consequently, most of the recent works are based on heuristic-based techniques conceding to approximate solutions rather than the best ones. In this chapter, the authors propose a solution using an iterative neural network that applies iterative spatial filtering technology with critically varied size of the computation window. With critical variation of the window size, the authors are able to show noted acceleration in the filtering approach (i.e., obtaining better quality filtration with lesser number of iterations).
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Among various extraction approaches the ones that perform better are the ones that behave in accord with human visual perceptions. Itti et al. (Bhattacharyya al., 2014) presented an approach in which Multi-scale image features are combined into a single topographical saliency map and thereafter a dynamical neural network selects the attended locations in order of decreasing saliency. Ma and Zhang (Ma Y. and Zhang H.,2003) proposed a feasible and fast approach to attention area detection in images based on contrast analysis. The main contributions are generation of a new saliency map through a method based on local contrast analysis followed by simulation of human perception as a fuzzy growing method to extract attended areas or objects from the saliency map; and finally a practicable framework has been presented for image attention analysis.

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