Abstract—Detection of blur in digital image, which is commonly preliminary step for de-blurring process, has becoming one of the growing research areas these days and has attracted many attentions from researchers. Research scholars have proposed new methods, or improved blur detection algorithms, based on edge sharpness analysis, low Depth of Field analysis, blind de-convolution, Bayes discriminant function, reference or non-reference block and wavelet based histogram with Support Vector Machine (SVM). The purpose of this paper is to explore the research trends (before year 1993 to year 2012) regarding the usage of blur detection algorithms for digital image processing researches. Because there are thousands of reliable literatures available, the trend is observed from the available online literature alone. Our scope of research has been limited only to search engine of IEEExplore®, ScienceDirect, and Google Scholar database. The searching for literatures will be classified according to their respective keyword for each method being utilized. We observed that low Depth of Field blur detection analysis is currently the most popular method, followed by edge sharpness analysis of blur detection. Google Scholar also has the most abundance source of online literature compared with IEEExplore® and ScienceDirect. Based on the trending graph, we observed that the researches in blur detection method are very positive, showing an overall increasing number of publication from year to year.

Index Terms—Blur detection, literature survey, edge sharpness analysis, low depth field analysis, blind de-convolution, Bayes discriminant function, non-reference block, wavelet based histogram

Cite: Boon Tatt Koik and Haidi Ibrahim, "Exploration of Current Trend on Blur Detection Method Utilized in Digital Image Processing," Journal of Industrial and Intelligent Information, Vol.1, No.3, pp. 143-147, September 2013. doi: 10.12720/jiii.1.3.143-147