Super resolution neural network with multiple outputs with different upscaling factors

    公开(公告)号:US11769226B2

    公开(公告)日:2023-09-26

    申请号:US17158553

    申请日:2021-01-26

    Inventor: Sheng Li Dongpei Su

    CPC classification number: G06T3/4046 G06T1/20 G06T3/4053 G06T3/4084

    Abstract: Systems and methods upscale an input image by a final upscaling factor. The systems and methods employ a first module implementing a super resolution neural network with feature extraction layers and multiple sets of upscaling layers sharing the feature extraction layers. The multiple sets of upscaling layers upscale the input image according to different respective upscaling factors to produce respective first module outputs. The systems and methods select the first module output with the respective upscaling factor closest to the final upscaling factor. If the respective upscaling factor for the selected first module output is equal to the final upscaling factor, the systems and methods output the selected first module output. Otherwise, the systems and methods provide the selected first module output to a second module that upscales the selected first module output to produce a second module output corresponding to the input image upscaled by the final upscaling factor.

    Optical character recognition training data generation for neural networks by parsing page description language jobs

    公开(公告)号:US10949664B2

    公开(公告)日:2021-03-16

    申请号:US16378470

    申请日:2019-04-08

    Inventor: Dongpei Su

    Abstract: Methods and apparatus for training and utilizing an artificial neural network (ANN) are provided. A computing device can receive training documents including text. The computing device can parse the training documents to determine training data items. Each training data item can include a training label related to text within the training documents and location information indicating a location of text related to the training label. An ANN can be trained to recognize text using the training data items and training input that includes the training documents. After training the ANN, a request to predict text in application documents that differ from the training documents can be received. The application documents can include second text. A prediction of the second text can be determined by applying the trained ANN to the application documents. After determining the prediction of the second text, information related to the second text can be provided.

    Selection of halftoning technique

    公开(公告)号:US10027847B2

    公开(公告)日:2018-07-17

    申请号:US15665706

    申请日:2017-08-01

    Abstract: An example embodiment may involve causing a page of a document to be printed on a printing device, wherein the printing device is in an AM halftoning mode and prints the page using an AM halftone; displaying, on the display unit, a graphical user interface, wherein the graphical user interface includes a selectable option to switch the printing device from the AM halftoning mode to an FM halftoning mode; receiving an indication that the selectable option has been selected; possibly in response to receiving the indication that the selectable option has been selected, causing the printing device to switch from the AM halftoning mode to the FM halftoning mode; and causing the page of the document to be printed again on the printing device, wherein the printing device is in the FM halftoning mode and prints the page using an FM halftone.

    Cell-based compression with edge detection and interleaved encoding
    8.
    发明授权
    Cell-based compression with edge detection and interleaved encoding 有权
    基于单元的压缩与边缘检测和交织编码

    公开(公告)号:US09363422B1

    公开(公告)日:2016-06-07

    申请号:US14610178

    申请日:2015-01-30

    Abstract: An example embodiment may involve obtaining (i) an a×b attribute macro-cell, and (ii) a×b pixel macro-cells for each of a luminance plane, a first color plane, and a second color plane of an input image. The a×b pixel macro-cells may each contain 4 non-overlapping m×n pixel cells. The example embodiment may also involve determining 4 attribute-plane output values that represent the 4 non-overlapping m×n attribute cells, 1 to 4 luminance-plane output values that represent the a×b pixel macro-cell of the luminance plane, a first color-plane output value to represent the a×b pixel macro-cell of the first color plane, and a second color-plane output value to represent the a×b pixel macro-cell of the second color plane. The example embodiment may further involve writing an interleaved representation of the output values to a computer-readable output medium.

    Abstract translation: 示例性实施例可以包括获得(i)a×b属性宏小区,以及(ii)输入图像的亮度平面,第一彩色平面和第二彩色平面中的每一个的×b像素宏小区 。 a×b像素宏单元可以各自包含4个非重叠的m×n个像素单元。 示例性实施例还可以包括确定表示4个非重叠m×n个属性单元的4个属性平面输出值,表示亮度平面的a×b个像素宏小区的1到4个亮度平面输出值, 第一颜色平面输出值,以表示第一彩色平面的a×b像素宏小区,以及第二颜色平面输出值,以表示第二彩色平面的a×b像素宏小区。 示例实施例还可以包括将输出值的交织表示写入计算机可读输出介质。

    Super-resolution convolutional neural network with gradient image detection

    公开(公告)号:US11366624B2

    公开(公告)日:2022-06-21

    申请号:US16834772

    申请日:2020-03-30

    Inventor: Sheng Li Dongpei Su

    Abstract: An example system includes a processor and a non-transitory computer-readable medium having stored therein instructions that are executable to cause the system to perform various functions. The functions include obtaining an image associated with a print job, and providing the image as input to a convolutional neural network. The convolutional neural network includes a residual network, upscaling layers, and classification layers configured to detect whether the image is an artificial image having a computer-generated image gradient. The functions also include determining, based on an output of the classification layers, that the image is an artificial image having a computer-generated image gradient. Further, the functions include, based on determining that the image is an artificial image having a computer-generated image gradient, providing the image to an upscaling module of a print pipeline for upscaling rather than using an output of the upscaling layers for the upscaling.

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