公开(公告)号：US20150262334A1

公开(公告)日：2015-09-17

申请号：US14434975

申请日：2013-11-06

申请人： PEKING UNIVERSITY ,  PEKING UNIVERSITY FOUNDER GROUP CO., LTD. ,  BEIJING FOUNDER ELECTRONICS CO., LTD.

发明人： Mading Li ,  Jiaying Liu ,  Jie Ren ,  Zongming Guo

IPC分类号： G06T3/40 ,  G06T5/50

CPC分类号： G06T3/4007 ,  G06T5/50 ,  G06T2207/20224

摘要： An image interpolation method and device based on an autoregressive model. The method first, interpolating a low-resolution image up to a target scale to obtain an interpolated image M; determining a local area W in the image M to be interpolated, establishing two autoregressive models for each pixel point in the local area W except for the edge pixel points, and determining an initial objective function F0 according to the autoregressive models; down sampling the local area W except for the edge pixel points to the same size as the low-resolution image to obtain a local area W′, subtracting a corresponding area in the low-resolution image from W′ one pixel value by one pixel value, and adding the result to the initial objective function F0 to obtain an objective function F; performing iteration on the objective function F to obtain a pixel point value of a center block of W.

摘要翻译： 一种基于自回归模型的图像插值方法和装置。 该方法首先将低分辨率图像内插到目标尺度以获得内插图像M; 确定要内插的图像M中的局部区域W，为除了边缘像素点之外的局部区域W中的每个像素点建立两个自回归模型，以及根据自回归模型确定初始目标函数F0; 将边缘像素点除以与低分辨率图像相同的尺寸的局部区域W进行下采样以获得局部区域W'，从W'一个像素值减去一个像素值的低分辨率图像中的对应区域 并将结果加到初始目标函数F0以获得目标函数F; 对目标函数F执行迭代以获得W的中心块的像素点值。

公开(公告)号：US09672590B2

公开(公告)日：2017-06-06

申请号：US14434975

申请日：2013-11-06

申请人： Peking University ,  Peking University Founder Group Co., Ltd. ,  Beijing Founder Electronics Co., Ltd.

发明人： Mading Li ,  Jiaying Liu ,  Jie Ren ,  Zongming Guo

IPC分类号： G06T3/40 ,  G06T5/50

CPC分类号： G06T3/4007 ,  G06T5/50 ,  G06T2207/20224

摘要： An image interpolation method and device based on an autoregressive model. The method first, interpolating a low-resolution image up to a target scale to obtain an interpolated image M; determining a local area W in the image M to be interpolated, establishing two autoregressive models for each pixel point in the local area W except for the edge pixel points, and determining an initial objective function F0 according to the autoregressive models; down sampling the local area W except for the edge pixel points to the same size as the low-resolution image to obtain a local area W′, subtracting a corresponding area in the low-resolution image from W′ one pixel value by one pixel value, and adding the result to the initial objective function F0 to obtain an objective function F; performing iteration on the objective function F to obtain a pixel point value of a center block of W.