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    • 84. 发明申请
    • Method of temporal noise reduction in video sequences
    • 视频序列中时间噪声降低的方法
    • US20060139494A1
    • 2006-06-29
    • US11025173
    • 2004-12-29
    • Zhi ZhouYeong-Taeg Kim
    • Zhi ZhouYeong-Taeg Kim
    • H04N5/21H04N5/14
    • H04N5/144H04N5/145H04N5/21
    • A motion-adaptive temporal noise reducing method and system for reducing noise in a sequence of video frames is provided. Temporal noise reduction is applied to two video frames, wherein one video frame is the current input noisy frame, and the other video frame is a previous filtered frame stored in memory. Once the current frame is filtered, it is saved into memory for filtering the next incoming frame. A motion-adaptive temporal filtering method is applied for noise reduction. Pixel-wise motion information between the current frame and the previous (filtered) frame in memory is examined. Then the pixels in the current frame are classified into motion region and non-motion region relative to the previous (filtered) frame. In a non-motion region, pixels in the current frame are filtered along the temporal axis. In a motion region, the temporal filter is switched off to avoid motion blurring.
    • 提供了一种用于降低视频帧序列中的噪声的运动自适应时间噪声降低方法和系统。 时间噪声降低被应用于两个视频帧,其中一个视频帧是当前输入噪声帧,而另一个视频帧是存储在存储器中的先前滤波的帧。 一旦当前帧被过滤,它被保存到存储器中,用于过滤下一个传入帧。 运动自适应时间滤波方法被应用于降噪。 检查存储器中当前帧与先前(滤波)帧之间的像素运动信息。 然后,当前帧中的像素相对于先前(滤波)的帧被分类为运动区域和非运动区域。 在非运动区域中,当前帧中的像素沿着时间轴被滤波。 在运动区域中,关闭时间滤波器以避免运动模糊。
    • 87. 发明申请
    • Methods of preventing noise boost in image contrast enhancement
    • 防止图像对比度增强中的噪声提升的方法
    • US20060013503A1
    • 2006-01-19
    • US10892775
    • 2004-07-16
    • Yeong-Taeg Kim
    • Yeong-Taeg Kim
    • G06K9/36
    • G06T5/002G06T5/009G06T5/40G06T2207/10016
    • An adaptive contrast enhancement method and device provide video signal contrast enhancement with reduced noise amplification. The video signal has a plurality of temporally ordered digital pictures, each one of the digital pictures represented by a set of samples, wherein each one of the samples has a gradation level. A contrast enhancement transform is constructed for enhancing the contrast of the video signal, and transform ratios are computed based on the contrast enhancement transform. Then the smoothed transform ratios are then applied to a set of samples representing a digital picture to enhance contrast of the digital picture with reduced noise amplification.
    • 自适应对比度增强方法和装置提供了具有减少的噪声放大的视频信号对比度增强。 视频信号具有多个时间排列的数字图像,每个数字图像由一组样本表示,其中每个样本具有灰度级。 构造对比度增强变换以增强视频信号的对比度,并且基于对比度增强变换来计算变换比。 然后将平滑的变换比率应用于表示数字图像的一组采样,以增强数字图像与降低的噪声放大的对比度。
    • 89. 发明申请
    • Method and apparatus for detecting the location and luminance transition range of slant image edges
    • 用于检测倾斜图像边缘的位置和亮度转变范围的方法和装置
    • US20050094877A1
    • 2005-05-05
    • US10697361
    • 2003-10-30
    • Xianglin WangYeong-Taeg Kim
    • Xianglin WangYeong-Taeg Kim
    • H04N5/208G06K9/00G06K9/48G06T5/00H04N5/14
    • H04N5/142G06T7/12G06T7/97G06T2207/10016
    • A system that detects the location as well as the luminance transition range of slant image edge in a digital image. The variance value of the pixels inside a rectangular image window centered with a current pixel is checked to determine if the current pixel is in an edge region or a in non-edge region. If the current pixel is in a non-edge region, no further checking is performed. Otherwise, it is determined if the current pixel is a center pixel in a luminance transition range of a slant edge. The values of the current pixel and its neighboring pixels inside the rectangular window are used to determine if the selected pixel is the center pixel in a luminance transition range of a slant edge. If it is, then the exact length of the luminance transition range of the slant edge is determined. Through such a detection process, both the center position and the luminance transition range of slant image edge can be determined.
    • 检测数字图像中倾斜图像边缘的位置以及亮度转变范围的系统。 检查以当前像素为中心的矩形图像窗口内的像素的方差值,以确定当前像素是处于边缘区域还是非边缘区域。 如果当前像素处于非边缘区域,则不进行进一步的检查。 否则,确定当前像素是否是倾斜边缘的亮度转变范围中的中心像素。 使用矩形窗口内的当前像素及其相邻像素的值来确定所选择的像素是否是倾斜边缘的亮度转变范围中的中心像素。 如果是,则确定倾斜边缘的亮度转变范围的确切长度。 通过这样的检测处理,可以确定倾斜图像边缘的中心位置和亮度转变范围。