[1] MEI S, YANG H, YIN Z P. Unsupervised-learning-based feature-level fusion method for Mura defect recognition[J]. IEEE Transactions on Semiconductor Manufacturing, 30, 105-113(2017).

[2] LI J, YIN Y M, MENG H. Research progress of color photoresists for TFT-LCD[J]. Dyes and Pigments, 225, 112094(2024).

[3] HU L, HU X J, HUANG Z H et al. Mura defect detection based on effective background reconstruction and contrast enhancement[J]. Chinese Journal of Liquid Crystals and Displays, 36, 1395-1402(2021).

[4] CEN Y G, ZHAO R Z, CEN L H et al. Defect inspection for TFT-LCD images based on the low-rank matrix reconstruction[J]. Neurocomputing, 149, 1206-1215(2015).

[5] PRATT W K, SAWKAR S S, O'REILLY K. Automatic blemish detection in liquid crystal flat panel displays[C], 2-13(1998).

[6] MING W Y, ZHANG S F, LIU X W et al. Survey of Mura defect detection in liquid crystal displays based on machine vision[J]. Crystals, 11, 1444(2021).

[7] FAN S K S, CHUANG Y C. Automatic detection of Mura defect in TFT-LCD based on regression diagnostics[J]. Pattern Recognition Letters, 31, 2397-2404(2010).

[8] LIU Y, ZHENG X R, WANG Y N et al. Application of MATLAB in TFT-LCD’s MURA defect detection[J]. Chinese Journal of Liquid Crystals and Displays, 22, 731-736(2007).

[9] TANIGUCHI K, UETA K, TATSUMI S. A Mura detection method[J]. Pattern Recognition, 39, 1044-1052(2006).

[10] LIN S Y, WU Y Q. Vision-based LCD/OLED defect detection methods: a critical summary[J]. Journal of Image and Graphics, 29, 1321-1345(2024).

[11] YAN C C. Research on detection technique for Mura defect of TFT-LCD[D](2017).

[12] SHI C Y. Mobile phone screen defect detection system based on machine vision[D](2017).

[13] WANG X, DONG R, LI B. TFT-LCD Mura defect detection based on ICA and multi-channels fusion[C], 687-691(2016).

[14] TSAI D M, LIN P C, LU C J. An independent component analysis-based filter design for defect detection in low-contrast surface images[J]. Pattern Recognition, 39, 1679-1694(2006).

[15] CASSIÈDE M, PAULY J, MILHET M et al. A quartz crystal microbalance technique to study wax crystallization in the presence of gas[J]. Measurement Science and Technology, 19, 065704(2008).

[16] MEI S. Research on TFT-LCD Mura defect recognition based on deep learning[D](2017).

[17] LIAO K C, WEN H T, WU H Y. Using image analysis techniques to enhance TFT-LCD manufacturing yield through timely detection of PI alignment layer defects[J]. Microelectronics Reliability, 151, 115259(2023).

[18] ZHENG G T, LIU P T, CHEN J L et al. Highly reliable a-Si:H gate driver on array with complementary double-sided noise-eliminating and dual voltage levels for TFT-LCD applications[J]. Journal of the Society for Information Display, 31, 638-650(2023).

[19] LEVESQUE P, SAWAN M. Novel low-power ultrasound digital preprocessing architecture for wireless display[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 57, 757-767(2010).

[20] YAN L Y, CENGIZ K, SHARMA A. An improved image processing algorithm for automatic defect inspection in TFT-LCD TCON[J]. Nonlinear Engineering, 10, 293-303(2021).

[21] XIA Y, LUO C, ZHOU Y J et al. A hybrid method of frequency and spatial domain techniques for TFT-LCD circuits defect detection[J]. IEEE Transactions on Semiconductor Manufacturing, 36, 45-55(2023).

[22] TSAI D M, HUANG C K. Defect detection in electronic surfaces using template-based fourier image reconstruction[J]. IEEE Transactions on Components, Packaging and Manufacturing Technology, 9, 163-172(2019).

[23] GE Z, LIU S T, WANG F et al. YOLOX: exceeding YOLO series in 2021[J/OL]. arXiv, 2107-08430(2021).

[24] LIN C H, TSAI J S, CHIU C T. Switching bilateral filter with a texture/noise detector for universal noise removal[J]. IEEE Transactions on Image Processing, 19, 2307-2320(2010).

[25] GOLESTAN S, RAMEZANI M, GUERRERO J M et al. Moving average filter based phase-locked loops: performance analysis and design guidelines[J]. IEEE Transactions on Power Electronics, 29, 2750-2763(2014).

[26] ZHU Y L, HUANG C. An improved median filtering algorithm for image noise reduction[J]. Physics Procedia, 25, 609-616(2012).

[27] DENG G, CAHILL L W. An adaptive Gaussian filter for noise reduction and edge detection[C], 1615-1619(1993).

[28] KUSNIK D, SMOLKA B. Robust mean shift filter for mixed Gaussian and impulsive noise reduction in color digital images[J]. Scientific Reports, 12, 14951(2022).

[29] BI X, DING H. Machine vision inspection method of mura defect for TFT-LCD[J]. Journal of Mechanical Engineering, 46, 13-19(2010).

[30] FENG Z H, LIN D Q, CHEN Z K et al. Vision-based electronic screen contrast adjustment method[P]. CN.

[31] LI K, LI H, LIU Y J et al. Background suppression of LCD Mura defect using B-spline surface fitting[J]. Opto-Electronic Engineering, 41, 33-39(2014).

[32] NGO C, PARK Y J, JUNG J et al. A new algorithm on the automatic TFT-LCD Mura defects inspection based on an effective background reconstruction[J]. Journal of the Society for Information Display, 25, 737-752(2017).

[33] CHAO S M, TSAI D M. Anisotropic diffusion with generalized diffusion coefficient function for defect detection in low-contrast surface images[J]. Pattern Recognition, 43, 1917-1931(2010).

[34] CHAO S M, TSAI D M. An anisotropic diffusion-based defect detection for low-contrast glass substrates[J]. Image and Vision Computing, 26, 187-200(2008).

[35] TSAI D M, HUNG C Y. Automatic defect inspection of patterned thin film transistor-liquid crystal display (TFT-LCD) panels using one-dimensional Fourier reconstruction and wavelet decomposition[J]. International Journal of Production Research, 43, 4589-4607(2005).

[36] TSAI D M, HSIEH C Y. Automated surface inspection for directional textures[J]. Image and Vision Computing, 18, 49-62(1999).

[37] TSAI D M, KUO C C. Defect detection in inhomogeneously textured sputtered surfaces using 3D Fourier image reconstruction[J]. Machine Vision and Applications, 18, 383-400(2007).

[38] JI C. Research on Mura defect detection technology of TFT-LCD panel based on background reconstruction[D](2019).

[39] PARK N K, CHOI K N, YOO S I. Quantification of line Mura defect levels based on multiple characterizing features[C], 606603(2006).

[40] ADAMO F, ATTIVISSIMO F, DI NISIO A. Calibration of an inspection system for online quality control of satin glass[J]. IEEE Transactions on Instrumentation and Measurement, 59, 1035-1046(2010).

[41] MARTÍNEZ S S, ORTEGA J G, GARCÍA J G et al. An industrial vision system for surface quality inspection of transparent parts[J]. The International Journal of Advanced Manufacturing Technology, 68, 1123-1136(2013).

[42] WANG W, LIU Z B, BAO Y et al. Application of digital image processing technology in scanning electrochemical microscope[J]. Chinese Journal of Analytical Chemistry, 46, 342-347(2018).

[43] SINGH T, DUA R L, AGRAWAL S et al. Detection of defects in glass sheet using C.S.C based segmentation method[J]. International Journal of Computer Applications, 68, 29-32(2013).

[44] LIN D Q, ZHAN Q X, ZHENG Z T et al. Development of machine vision inspection system for panel module of vehicle touch screen[J]. Electronic Measurement Technology, 45, 153-158(2022).

[45] LEE J Y, YOO S I. Automatic detection of region-Mura defect in TFT-LCD[J]. IEICE Transactions on Information and Systems, E87-D, 2371-2378(2004).

[46] YANG S W, LIN C S, LIN S K et al. Automatic defect recognition of TFT array process using gray level co-occurrence matrix[J]. Optik, 125, 2671-2676(2014).

[47] MING W Y, SHEN F, LI X K et al. A comprehensive review of defect detection in 3C glass components[J]. Measurement, 158, 107722(2020).

[48] ZHOU C R, WANG Z T, YANG S W et al. Application progress of chemometrics and deep learning methods in raman spectroscopy signal processing[J]. Chinese Journal of Analytical Chemistry, 51, 1232-1242(2023).

[49] YOUSEFIAN-JAZI A, RYU J H, YOON S et al. Decision support in machine vision system for monitoring of TFT-LCD glass substrates manufacturing[J]. Journal of Process Control, 24, 1015-1023(2014).

[50] LI D, LIANG L Q, ZHANG W J. Defect inspection and extraction of the mobile phone cover glass based on the principal components analysis[J]. The International Journal of Advanced Manufacturing Technology, 73, 1605-1614(2014).

[51] LIU Y H, LIN S H, HSUEH Y L et al. Automatic target defect identification for TFT-LCD array process inspection using kernel FCM-based fuzzy SVDD ensemble[J]. Expert Systems with Applications, 36, 1978-1998(2009).

[52] CHEN P, CHEN M F, WANG S et al. Real-time defect detection of TFT-LCD displays using a lightweight network architecture[J]. Journal of Intelligent Manufacturing, 35, 1337-1352(2024).

[53] LIN G M, KONG L F, LIU T J et al. An antagonistic training algorithm for TFT-LCD module Mura defect detection[J]. Signal Processing: Image Communication, 107, 116791(2022).

[54] SONG S B, YANG K C, WANG A N et al. A mura detection model based on unsupervised adversarial learning[J]. IEEE Access, 9, 49920-49928(2021).

[55] LU H P, SU C T. CNNs combined with a conditional GAN for Mura defect classification in TFT-LCDs[J]. IEEE Transactions on Semiconductor Manufacturing, 34, 25-33(2021).

[56] MEI S, YANG H, YIN Z P. An unsupervised-learning-based approach for automated defect inspection on textured surfaces[J]. IEEE Transactions on Instrumentation and Measurement, 67, 1266-1277(2018).

[57] YANG H, CHEN Y F, SONG K Y et al. Multiscale feature-clustering-based fully convolutional autoencoder for fast accurate visual inspection of texture surface defects[J]. IEEE Transactions on Automation Science and Engineering, 16, 1450-1467(2019).

[58] YANG H, ZHOU Q Y, SONG K Y et al. An anomaly feature-editing-based adversarial network for texture defect visual inspection[J]. IEEE Transactions on Industrial Informatics, 17, 2220-2230(2021).

[59] LIU Z B, ZHANG S, BAO Y et al. Progress of application research on cheminformatics in deep learning[J]. Chinese Journal of Applied Chemistry, 40, 360-373(2023).

[60] YANG H, MEI S, SONG K Y et al. Transfer-learning-based online Mura defect classification[J]. IEEE Transactions on Semiconductor Manufacturing, 31, 116-123(2018).

[61] FAN X H. Research on surface defect detection method of LCD screen based on deep learning[D](2022).