Abstract: An automatic optical inspection device, including an image storage unit; an image computing unit; and an image acquisition unit. The image storage unit includes a first communication interface and a second communication interface. The image computing unit includes a first optical interface, a second optical interface, a third optical interface, and a fourth optical interface; the image acquisition unit includes a third communication interface and a camera interface. The image storage unit is configured to transmit configuration parameters and test commands to the image computing unit, receive a test result transmitted from the image computing unit via the first communication interface, and receive data from the image acquisition unit via the second communication interface. The image computing unit is configured to receive the configuration parameters and test commands from the image storage unit, and transmit the test result to the image storage unit via the first fiber interface.

Abstract: An image acceleration processing device including a field programmable gate array (FPGA) processing platform; and a personal computer (PC). The FPGA processing platform includes: a first fiber interface configured to receive configuration parameters and test commands, and to output test results; a second fiber interface configured to exchange data with the PC; a third fiber interface configured to receive image data and output the configuration parameters and the test commands; a fourth fiber interface configured to control the generation of a screen lighting signal; and a fifth fiber interface configured to control an input/output (IO) light source.

Abstract: An image acceleration processing device including a field programmable gate array (FPGA) processing platform; and a personal computer (PC). The FPGA processing platform includes: a first fiber interface configured to receive configuration parameters and test commands, and to output test results; a second fiber interface configured to exchange data with the PC; a third fiber interface configured to receive image data and output the configuration parameters and the test commands; a fourth fiber interface configured to control the generation of a screen lighting signal; and a fifth fiber interface configured to control an input/output (IO) light source.

Abstract: An automatic optical inspection device, including an image storage unit; an image computing unit; and an image acquisition unit. The image storage unit includes a first communication interface and a second communication interface. The image computing unit includes a first optical interface, a second optical interface, a third optical interface, and a fourth optical interface; the image acquisition unit includes a third communication interface and a camera interface. The image storage unit is configured to transmit configuration parameters and test commands to the image computing unit, receive a test result transmitted from the image computing unit via the first communication interface, and receive data from the image acquisition unit via the second communication interface. The image computing unit is configured to receive the configuration parameters and test commands from the image storage unit, and transmit the test result to the image storage unit via the first fiber interface.

Abstract: The disclosure provides a background suppression method and a detecting device in automatic optical detection for a display panel, wherein the background suppression method includes the following steps: S1: collecting a pure color image of the display panel; S2: performing multi-level wavelet decomposition on the pure color image to obtain a series of high-frequency sub-bands and low-frequency sub-bands; S3: performing coefficient smoothing process on high-frequency sub-bands in multiple directions of each level, and performing contrast enhancement process on each level of low-frequency sub-bands; S4: the processed high-frequency sub-bands and the processed low-frequency sub-bands are subjected to wavelet reconstruction to obtain a defect image after background suppression.

Abstract: The disclosure provides a background suppression method and a detecting device in automatic optical detection for a display panel, wherein the background suppression method includes the following steps: S1: collecting a pure color image of the display panel; S2: performing multi-level wavelet decomposition on the pure color image to obtain a series of high-frequency sub-bands and low-frequency sub-bands; S3: performing coefficient smoothing process on high-frequency sub-bands in multiple directions of each level, and performing contrast enhancement process on each level of low-frequency sub-bands; S4: the processed high-frequency sub-bands and the processed low-frequency sub-bands are subjected to wavelet reconstruction to obtain a defect image after background suppression.

Abstract: The present disclosure discloses a GPU-based TFT-LCD Mura defect detection method, comprising: (1) establishing a studentized residual based double-second-order regression diagnosis model based original image data to obtain double-second-order regression background data; (2) obtaining influence quantities of respective data points on fitted values according to the original image data and the double-second-order regression background image data; (3) excluding outliers and influential points in the original image data according to the influence quantities to obtain a new pixel point set; (4) establishing a double-N-order polynomial surface fitting model according to the new pixel point set to obtain double-N-order background image data; (5) obtaining a residual image R according to the double-N-order background image data and the original image data, and performing threshold segmentation on the residual image to obtain a threshold segmentation image; and (6) performing morphological processing on the threshold

Abstract: The present disclosure discloses a GPU-based TFT-LCD Mura defect detection method, comprising: (1) establishing a studentized residual based double-second-order regression diagnosis model based original image data to obtain double-second-order regression background data; (2) obtaining influence quantities of respective data points on fitted values according to the original image data and the double-second-order regression background image data; (3) excluding outliers and influential points in the original image data according to the influence quantities to obtain a new pixel point set; (4) establishing a double-N-order polynomial surface fitting model according to the new pixel point set to obtain double-N-order background image data; (5) obtaining a residual image R according to the double-N-order background image data and the original image data, and performing threshold segmentation on the residual image to obtain a threshold segmentation image; and (6) performing morphological processing on the threshold