Abstract: The embodiments of the present application provide method, apparatus, electronic device, and medium for image super-resolution and model training. The method includes: inputting the image to be processed into a first super-resolution network model and a second super-resolution network model trained in advance, respectively; the first super-resolution network model is a trained convolutional neural network; the second super-resolution network model is a generative network included in a trained generative adversarial network; obtaining a first image output from the first super-resolution network model and a second image output from the second super-resolution network model; fusing the first image and the second image to obtain a target image, wherein the resolution of the target image is greater than the resolution of the image to be processed.

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