井冈山大学学报(自然科学版)
井岡山大學學報(自然科學版)
정강산대학학보(자연과학판)
JOURNAL OF JINGGANGSHAN UNIVERSITY(SCIENCE AND TECHNOLOGY)
2013年
6期
44-50
,共7页
李光耀%耿瑞全%谭云兰%肖莽
李光耀%耿瑞全%譚雲蘭%肖莽
리광요%경서전%담운란%초망
图像修复%区域分割%均方误差%纹理合成
圖像脩複%區域分割%均方誤差%紋理閤成
도상수복%구역분할%균방오차%문리합성
image completion%region segmentation%sum of squared differences%texture synthesis
基于样本块的 Criminisi 图像修复算法在搜索匹配块时,使用全局搜索并用均方误差(sum of squared differences)来衡量样本块差异。该方法存在搜索范围过大,效率较低,仅考虑颜色的差异,容易导致修复结果边界错位等不足,本文提出了一种基于区域分割和均方误差改进的图像修复算法。为了提高样本块匹配速度,先采用区域分割法分割整个图像区域,使待修复样本块只在对应区域内搜索。在比较样本块差异时,本文算法对颜色差异、纹理差异、曲线特征差异进行了加权综合,从而保证了修复后图像在颜色和纹理上均与已知区域保持一致,解决了Criminisi算法效率低且容易出错等问题。实验结果表明本文算法修复结果在执行效率、视觉效果上要比Criminisi算法好。
基于樣本塊的 Criminisi 圖像脩複算法在搜索匹配塊時,使用全跼搜索併用均方誤差(sum of squared differences)來衡量樣本塊差異。該方法存在搜索範圍過大,效率較低,僅攷慮顏色的差異,容易導緻脩複結果邊界錯位等不足,本文提齣瞭一種基于區域分割和均方誤差改進的圖像脩複算法。為瞭提高樣本塊匹配速度,先採用區域分割法分割整箇圖像區域,使待脩複樣本塊隻在對應區域內搜索。在比較樣本塊差異時,本文算法對顏色差異、紋理差異、麯線特徵差異進行瞭加權綜閤,從而保證瞭脩複後圖像在顏色和紋理上均與已知區域保持一緻,解決瞭Criminisi算法效率低且容易齣錯等問題。實驗結果錶明本文算法脩複結果在執行效率、視覺效果上要比Criminisi算法好。
기우양본괴적 Criminisi 도상수복산법재수색필배괴시,사용전국수색병용균방오차(sum of squared differences)래형량양본괴차이。해방법존재수색범위과대,효솔교저,부고필안색적차이,용역도치수복결과변계착위등불족,본문제출료일충기우구역분할화균방오차개진적도상수복산법。위료제고양본괴필배속도,선채용구역분할법분할정개도상구역,사대수복양본괴지재대응구역내수색。재비교양본괴차이시,본문산법대안색차이、문리차이、곡선특정차이진행료가권종합,종이보증료수복후도상재안색화문리상균여이지구역보지일치,해결료Criminisi산법효솔저차용역출착등문제。실험결과표명본문산법수복결과재집행효솔、시각효과상요비Criminisi산법호。
Image inpainting is one of important and challenging research topics in computer graphics, video processing, and computer vision. It provides a strong tool for the reuse of captured images and photos. It also shows extensive applications in cultural heritage protection, special visual effects, image and video editing and virtual reality. The traditional Criminisi based patch image completion algorithms only consider the intensity difference when computing the difference within patches and search the most similar exemplar patches in the source region of image, the computation is too large. A new difference measure for completion is presented. This measure considers the intensity difference, texture difference and curve difference when computing the difference within patches. It successfully overcomes the drawbacks the general intensity difference method has, ensuring the content continuity within the textures and retaining perceptual coherence in synthesized texture and inpainted image. The experiment shows the result inpainted images using our algorithm are better than Criminisi algorithm, and the repair time also has a considerable decrease.