图像分割是计算机视觉中基础且重要的一个问题.熵阈值图像分割作为一种有效的分割方法,被广泛应用于模式识别和图像处理中.传统的图像分割方法并不能获得足够有效的图像特征.为解决这个问题且进一步探究熵阈值在图像分割中的应用,引入一...图像分割是计算机视觉中基础且重要的一个问题.熵阈值图像分割作为一种有效的分割方法,被广泛应用于模式识别和图像处理中.传统的图像分割方法并不能获得足够有效的图像特征.为解决这个问题且进一步探究熵阈值在图像分割中的应用,引入一种GLLE(Gray Level and Local Entropy)二维直方图改进熵阈值图像分割模型,并提出了基于模糊熵的方法计算所建立的二维直方图模型.通过标准实验数据集上的对比实验表明,基于模糊熵的GLLE熵阈值分割方法可以得到更加准确的阈值,提高了分割精度.同时在处理不同类型图像的表现上优于往常的算法,具有更强的鲁棒性.展开更多
Video Super-Resolution(SR) reconstruction produces video sequences with High Resolution(HR) via the fusion of several Low-Resolution(LR) video frames.Traditional methods rely on the accurate estimation of subpixel mot...Video Super-Resolution(SR) reconstruction produces video sequences with High Resolution(HR) via the fusion of several Low-Resolution(LR) video frames.Traditional methods rely on the accurate estimation of subpixel motion,which constrains their applicability to video sequences with relatively simple motions such as global translation.We propose an efficient iterative spatio-temporal adaptive SR reconstruction model based on Zernike Moment(ZM),which is effective for spatial video sequences with arbitrary motion.The model uses region correlation judgment and self-adaptive threshold strategies to improve the effect and time efficiency of the ZM-based SR method.This leads to better mining of non-local selfsimilarity and local structural regularity,and is robust to noise and rotation.An efficient iterative curvature-based interpolation scheme is introduced to obtain the initial HR estimation of each LR video frame.Experimental results both on spatial and standard video sequences demonstrate that the proposed method outperforms existing methods in terms of both subjective visual and objective quantitative evaluations,and greatly improves the time efficiency.展开更多
文摘图像分割是计算机视觉中基础且重要的一个问题.熵阈值图像分割作为一种有效的分割方法,被广泛应用于模式识别和图像处理中.传统的图像分割方法并不能获得足够有效的图像特征.为解决这个问题且进一步探究熵阈值在图像分割中的应用,引入一种GLLE(Gray Level and Local Entropy)二维直方图改进熵阈值图像分割模型,并提出了基于模糊熵的方法计算所建立的二维直方图模型.通过标准实验数据集上的对比实验表明,基于模糊熵的GLLE熵阈值分割方法可以得到更加准确的阈值,提高了分割精度.同时在处理不同类型图像的表现上优于往常的算法,具有更强的鲁棒性.
基金the National Basic Research Program of China (973 Program) under Grant No.2012CB821200,the National Natural Science Foundation of China under Grants No.91024001,No.61070142,the Beijing Natural Science Foundation under Grant No.4111002
文摘Video Super-Resolution(SR) reconstruction produces video sequences with High Resolution(HR) via the fusion of several Low-Resolution(LR) video frames.Traditional methods rely on the accurate estimation of subpixel motion,which constrains their applicability to video sequences with relatively simple motions such as global translation.We propose an efficient iterative spatio-temporal adaptive SR reconstruction model based on Zernike Moment(ZM),which is effective for spatial video sequences with arbitrary motion.The model uses region correlation judgment and self-adaptive threshold strategies to improve the effect and time efficiency of the ZM-based SR method.This leads to better mining of non-local selfsimilarity and local structural regularity,and is robust to noise and rotation.An efficient iterative curvature-based interpolation scheme is introduced to obtain the initial HR estimation of each LR video frame.Experimental results both on spatial and standard video sequences demonstrate that the proposed method outperforms existing methods in terms of both subjective visual and objective quantitative evaluations,and greatly improves the time efficiency.