[1] 刘涛, 李永峰, 黄威. BP神经网络在红外热波无损检测定量识别中的应用[J]. 红外与激光工程, 2012, 41(9): 2304-2310.

    Liu Tao, Li Yongfeng, Huang Wei. Application of BP neural network to quantitative identification in thermal wave NDT[J]. Infrared and Laser Engineering, 2012, 41(9): 2304-2310. (in Chinese)

[2] Liu J, Tang Q, Yang W, et al. Defects′ geometric feature recognition based on infrared image edge detection[J]. Infrared Physics & Technology, 2014, 67(4): 387-390.

[3] 宋远佳, 张炜, 王冬冬,等. 热波检测缺陷定量识别与图像重建研究[J]. 材料工程, 2012, 2(5): 39-46.

    Song Yuanjia, Zhang Wei, Wang Dongdong, et al. Defect quantitative identification and image reconstruction based on thermal wave inspection[J]. Journal of Materials Engineering, 2012, 2(5): 39-46. (in Chinese)

[4] Branchitta F, Diani M, Romagnoli M. New technique for the visualization of high dynamic range infrared images[J]. Optical Engineering, 2009, 48(9): 6401.

[5] Kapur J N, Sahoo P K, Wong A K C. A new method for gray-level picture thresholding using the entropy of the histogram[J]. Computer Vision Graphics & Image Processing, 1985, 29(3): 273-285.

[6] 金明磊, 范永杰, 金伟其, 等. IRFPA的自适应细节增强算法及其硬件实现[J]. 光学技术, 2013, 39(2): 163-167.

    Jin Minglei, Fan Yongjie, Jin Weiqi, et al. Adaptive smoothing and dynamic range partitioning compression algorithm for IRFPA and hardware implementation[J]. Optical Technique, 2013, 39(2): 163-167. (in Chinese)

[7] Zhang A F, Jiang D. Adaptive contrast enhancement by a Gaussian function of local standard deviation[J]. Image Matching & Analysis, 2001: 7-11.

[8] 汪筱红, 须文波. 遗传算法在最大熵多阈值分割的应用研究[J]. 贵州大学学报, 2007, 24(4): 401-403.

    Wang Xiaohong, Xu Wenbo. Study on maximum entropy multilevel threshold segmentation based on genetic algorithm[J]. Journal of Guizhou University, 2007, 24(4): 401-403. (in Chinese)

[9] 冯彦铭. 基于图像处理技术的异形件几何尺寸测量及缺陷检测[D]. 衡阳: 南华大学, 2015.

    Feng Yanming. The special-shape parts geometric dimension shape parts geometric dimension measurement and defect detection based on image processing technology[D]. Hengyang: University of South China, 2015. (in Chinese)

[10] Song X, Pogue B W, Jiang S, et al. Automated region detection based on the contrast-to-noise ratio in near-infrared tomography[J]. Applied Optics, 2004, 43(5): 1053-1062.