Abstract
Aiming at the problem that commonly used image definition evaluation functions in the focusing process are sensitive to noise, we propose a new image definition evaluation function based on improved maximum local variation and focusing window selection. Firstly, the focusing window is selected by gradient accumulation of a 4-directional Scharr operator in order to reduce the calculation complexity and improve the accuracy of evaluation results. Secondly, an improved 3-neighbors method based on the maximum local variation is proposed to decrease the change in scores for noisy images. Finally, the standard deviation of the improved maximum local variation distribution is used as the measure of clarity. The experimental results show that compared with the method using maximum local variation, the proposed method has better unbiasedness and sensitivity. Compared with the commonly used evaluation functions, the proposed method has better noise immunity and high sensitivity. Compare with other no-reference image quality assessment algorithms, it has better monotonicity, unimodality, unbiasedness, sensitivity and real-time performance as well. The proposed method is suitable for the fine focusing stage with high real-time performance.
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Acknowledgement
This work was supported by the Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (MJUKF-IPIC202110), National Natural Science Foundation of China (61972187), Natural Science Foundation of Fujian Province (2020J02024), Fuzhou Science and Technology Project (2020-RC-186), Research Project of Undergraduate Teaching Reform in Fujian University of Technology (JG2021020).
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Li, S., Chen, J., Wan, J., Li, Z., Lin, L. (2021). Image Definition Evaluation Function Based on Improved Maximum Local Variation and Focusing Window Selection. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7_9
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DOI: https://doi.org/10.1007/978-981-16-7502-7_9
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