基于曝光因子的自适应SOFC图像增强
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摘要
为提高图像分析法中的图像分割方法精度,更好地分析SOFC微观结构,对曝光图像增强的预处理方法进行研究,提出一种基于曝光因子的自适应图像增强方法。递归迭代计算直方图曝光阈值,利用输入图像的曝光因子值自适应计算直方图限幅阈值,根据限幅阈值进行直方图限幅,通过分切阈值将直方图分切成若干个子直方图,结合改进的灰度概率计算方法进行子直方图均衡,实现图像的增强。实验结果表明,该方法相较已有图方法能够更好地保持像细节信息和亮度,具有较好的自适应性、鲁棒性。
To improve the image segmentation accuracy in image analysis and better analyze the microstructure of SOFC,apreprocessing method of exposure image enhancement was studied,and an adaptive image enhancement method based on exposure factor was proposed.The threshold of the histogram was recursively calculated.The limit threshold of the histogram was adaptively calculated by the exposure factor value of the input image and the histogram was clipped according to the limit threshold.The histogram was divided into several sub-histograms with the exposure thresholds.The sub-histogram equalization was performed in conjunction with the improved gray-scale probability calculation method,the image enhancement was accomplished.Experimental results show that the proposed method can preserve the detail information and brightness better than the existing methods,and it has good adaptability and robustness.
To improve the image segmentation accuracy in image analysis and better analyze the microstructure of SOFC,apreprocessing method of exposure image enhancement was studied,and an adaptive image enhancement method based on exposure factor was proposed.The threshold of the histogram was recursively calculated.The limit threshold of the histogram was adaptively calculated by the exposure factor value of the input image and the histogram was clipped according to the limit threshold.The histogram was divided into several sub-histograms with the exposure thresholds.The sub-histogram equalization was performed in conjunction with the improved gray-scale probability calculation method,the image enhancement was accomplished.Experimental results show that the proposed method can preserve the detail information and brightness better than the existing methods,and it has good adaptability and robustness.
引文
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[2]Fu X,Xiang Y,Chen L,et al.Solid oxide fuel cell anode image segmentation based on a novel quantum-inspired fuzzy clustering[J].Journal of Power Sources,2015,300:57-68.
[3]Li Z,Wei Z,Wen C,et al.Detail-enhanced multi-scale exposure fusion[J].IEEE Transactions on Image Processing a Publication of the IEEE Signal Processing Society,2017,26(3):1243-1252.
[4]Singh K,Kapoor R.Image enhancement using exposure based sub image histogram equalization[J].Pattern Recognition Letters,2014,36(1):10-14.
[5]Singh K,Kapoor R.Image enhancement via median-mean based sub-image-clipped histogram equalization[J].Optik-International Journal for Light and Electron Optics,2014,125(17):4646-4651.
[6]Singh K,Kapoor R,Sinha SK.Enhancement of low exposure images via recursive histogram equalization algorithms[J].Optik-International Journal for Light and Electron Optics,2015,126(20):2619-2625.
[7]Jing RT,Isa NAM.Intensity exposure-based bi-histogram equalization for image enhancement[J].Turkish Journal of Electrical Engineering&Computer Sciences,2016,24(5):3564-3585.
[8]Dhal KG,Sen S,Sarkar K,et al.Entropy based range optimized brightness preserved histogram-equalization for image contrast enhancement[J].International Journal of Swarm Intelligence Research,2016,6(1):59-72.
[9]Tang JR,Isa NAM.Bi-histogram equalization using modified histogram bins[J]. Applied Soft Computing,2017,55:31-43.
[10]FU Xiaowei,DAI Yun,CHEN Li,et al.Quantum-inspired despeckling of medical ultrasound images based on local entropy[J].Journal of Electronics&Information Technology,2015,37(3):560-566(in Chinese).[付晓薇,代芸,陈黎,等.基于局部熵的量子衍生医学超声图像去斑[J].电子与信息学报,2015,37(3):560-566.]
[11]Parihar AS,Verma OP.Contrast enhancement using entropybased dynamic sub-histogram equalisation[J].Iet Image Processing,2017,10(11):799-808.