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    Title: 影片及圖像之亮度修正
    Other Titles: Luminance adjustment in image and video
    Authors: 蔡文儒;Tsai, Wen-ju
    Contributors: 淡江大學資訊工程學系資訊網路與通訊碩士班
    施國琛;Shih, Timothy K.
    Keywords: 亮度修補;平均亮度;動態區域直方圖均衡化;intensity;luminance;histogram equalization;average intensity
    Date: 2008
    Issue Date: 2010-01-11 06:16:26 (UTC+8)
    Abstract: 透過影片的亮度平均修正後,拉近了影片中各圖像的亮度值,進而消弭亮度高低差異,因此影片閃爍的現象即可消除,而亮度值皆在調整後針對影片中各個影像進行細部的微調,調整圖像中亮度對比,使圖像中細部的顏色呈現一個協調的色彩。
    色彩亮度校正主要是針對影片中的連續圖像,由於拍攝中光源的不穩定而有可能產生一樣的場景但是圖像中的亮度差異卻有所不同,進而在進行影像修補與重建時該圖像的前後張也會一同列入修補的可能情況,因此在貼補的過程中,會有不自然不協調的情況發生,而為求達到正確的修補置入物件,而在此特地針對這個問題進行修正,讓修補置入的影像可以有更佳效果呈現。
    After luminance fixed in image and video, luminance in image and video can be similar. Removed luminance difference, then we can see the clearly image with accordant luminance. And we can detail adjust intensity in video or frames, then we can show the modify intensity value in our experiment. After inpaint the intensity in image or video, the image will display the new intensity. We can see the image different and better than original intensity. We maybe also adjust intensity contrast, make sure the detail color and luminance appear same coordinate color. In this issue, we try to use average intensity to solve luminance in video, permeate the frames average intensity we can calculate the middle intensity then we can get the threshold help us to fix the image intensity. And we can use histogram and tensor voting to solve luminance difference in image. We will show the experiment in this research. Then, we will compare these two methods, the result in histogram and tensor voting.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

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