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    • 15. 发明申请
    • Image Amplifying Method, Image Amplifying Device, and Display Apparatus
    • 图像放大方法,图像放大装置及显示装置
    • US20160364840A1
    • 2016-12-15
    • US14771340
    • 2015-01-04
    • BOE TECHNOLOGY GROUP CO., LTD.
    • Lijie Zhang
    • G06T3/40H04N1/60G06T5/50
    • G06T3/4007G06T3/40G06T3/4053G06T5/50G06T2207/10024G06T2207/20064G06T2207/20221H04N1/3876H04N1/6008
    • Embodiments of the disclosure provide an image amplifying method, an image amplifying device, and a display apparatus, and relate to field of image processing technique, the method comprises: obtaining, by an image amplifying device, high-frequency and low-frequency components of a source image; performing, by the image amplifying device, pixel interpolation on the low-frequency components of the source image through a first interpolation algorithm, to obtain a low-frequency sub-image; performing, by the image amplifying device, pixel interpolation on the high-frequency components of the source image through a second interpolation algorithm, to obtain a high-frequency sub-image; and merging, by the image amplifying device, the low-frequency and high-frequency sub-images, to obtain a merged image; wherein the first interpolation algorithm and the second interpolation algorithm adopt different algorithms, so that it can ensure image quality of the amplified image while reducing the operation amount. Embodiments of the disclosure are applied to image amplification.
    • 本发明的实施例提供一种图像放大方法,图像放大装置和显示装置,涉及图像处理技术领域,该方法包括:通过图像放大装置获得高频和低频分量的高频和低频分量 源图像; 通过图像放大装置通过第一插值算法对源图像的低频分量进行像素插值,以获得低频子图像; 通过图像放大装置通过第二插值算法对源图像的高频分量进行像素插值,以获得高频子图像; 并通过图像放大装置合并低频和高频子图像,以获得合并图像; 其中第一插值算法和第二插值算法采用不同的算法,从而可以在减少操作量的同时确保放大图像的图像质量。 本公开的实施例应用于图像放大。
    • 16. 发明授权
    • Frequency signal generating system and display device
    • 频率信号发生系统和显示装置
    • US09425810B2
    • 2016-08-23
    • US14361405
    • 2013-12-20
    • BOE Technology Group Co., Ltd.Beijing BOE Display Technology Co., Ltd.
    • Xitong MaXiao ZhangShuhuan YuLijie Zhang
    • H03L7/06H03L7/18H03L7/099
    • H03L7/18H03L7/0992H03L7/183H03L7/193
    • A frequency signal generating system comprises a digital phase-locked loop for receiving a source frequency signal; a loop filter for filtering out high frequency components of a signal output from the digital phase-locked loop; and a voltage controlled oscillator for outputting a target frequency signal according to a signal from the loop filter, wherein an output terminal of the voltage controlled oscillator is connected to a first output terminal of the digital phase-locked loop so that the target frequency signal output from the voltage controlled oscillator is fed back to the digital phase-locked loop, the digital phase-locked loop performs frequency-dividing and phase-detecting on the source frequency signal and the fed back target frequency signal so that the target frequency signal output from the voltage controlled oscillator and the source frequency signal satisfy a definite mathematical relationship therebetween.
    • 频率信号发生系统包括用于接收源频率信号的数字锁相环; 用于滤除从数字锁相环输出的信号的高频分量的环路滤波器; 以及压控振荡器,用于根据来自环路滤波器的信号输出目标频率信号,其中压控振荡器的输出端连接到数字锁相环的第一输出端,​​使得目标频率信号输出 从压控振荡器反馈到数字锁相环,数字锁相环对源极频率信号和反馈目标频率信号进行分频和相位检测,使得目标频率信号从 压控振荡器和源极频率信号之间具有确定的数学关系。
    • 19. 发明授权
    • Image segmentation apparatus, method and relevant computing device
    • US11113816B2
    • 2021-09-07
    • US16651946
    • 2019-09-19
    • BOE TECHNOLOGY GROUP CO., LTD.
    • Guannan ChenLijie Zhang
    • G06K9/46G06T7/10G06N3/02
    • The present disclosure provides an image segmentation apparatus, method and relevant computing device. The image segmentation apparatus comprises: a feature extractor configured to extract N image semantic features having different scales from an input image, where N is an integer not less than 3; and a feature processor comprising cascaded dense-refine networks and being configured to perform feature processing on the N image semantic features to obtain a binarized mask image for the input image. A dense-refine network is configured to generate a low-frequency semantic feature from semantic features input thereto by performing densely-connected convolution processing on the semantic features respectively to obtain respective image global features, performing feature fusion on the image global features to obtain a fused image global feature, and performing pooling processing on the fused image global feature to generate and output the low-frequency semantic feature. The semantic features are selected from a group consisting of the N image sematic features and low-frequency semantic features generated by dense-refine networks. The feature processor is configured to obtain the binarized mask image based on low-frequency semantic features generated by the dense-refine networks.