Fast parallel blur detection of digital images
Current popularity of digital camera and smartphone leads to an enormous amount of digital images. This raises a demand for image quality assessment, one of which is about blur detection. In this paper, we analyze existing sequential blur detection scheme using Haar Wavelet Transform. Using several predefined rules, edge detection is then performed in this wavelet analysis to determine if the input image is blurred or not, and to what extent it is blurred. Based on this blur detection scheme, we propose a parallelized approach for detecting blurry images inside a dataset. Our approach mainly focuses on decoupling data dependencies and partitioning data inside each step. Our experimental results demonstrate the effectiveness of our parallel approach up to 50% faster than traditional, sequential algorithm with comparable accuracy.
2016 IEEE RIVF International Conference on Computing Communication Technologies, Research, Innovation, and Vision for the Future (RIVF)