基于···度机···像超···重构

项目来源

国家···学基···SF··

项目主持人

石爱·

项目受资助机构

河海··

立项年度

2008

立项时间

未公开

项目编号

60···09·

研究期限

未知 / 未知

项目级别

国家·

受资助金额

28.00万元

学科

信息··-电子···息系·-图像···理

学科代码

F-F01-F0116

基金类别

面上··

关键词

智能···理;···眼;···度机···像处···分辨···;

英文关键词

im···pr···ss···;i···ll···nt···fo···ti···pr···ss···;s···rr···lu···n ···on···uc···n;···ni···om···nd···e;···er···it··

参与者

黄陈···国芳···民;···;汪···敏;···;

参与机构

南京···院;

项目标书摘要:借鉴···眼的···理及···觉的···度机···究基···复眼···"策···视锐···、自···准和···经网···计算···超分···构方···术,···非生···的常···方法···。研···包括···)序···的超···蝇类···觉仿···系统···型)···)"···图像···配准···的预···(3···超视···理和···络重···。(···于蝇···视觉···池细···式的···态更···据缺···度管···。(···拟的···虫视···超分···构系···中的···术。

Th···om···nd···es···d ···ir···pe···ui···of···yi···in···ts···e ···hl···ff···en···t ···ce···ng···ce···d ···or···io···he···se···h ···s ···im···te···is···ti···an···ut···t ··· s···rr···lu···n ···on···uc···n(···)m···od···as···on···rt··· c···ou···ey···yp···cu···,a···ti···re···tr···on···d ···ra···et···k ···as··· o···co···th···ho···om···s ···th···rd···ry···R ···ho···Wo···co···ed··· t···pr···ct···cl···s:··· t···ni···s ··· m···l ···se···nc···im···s ···ed··· t···hy···ac···y ···in···t,···tu···om···id··· i···es···ap···e ···is···ti···an···at···re···ce···ng···R ···ho···ba··· o···yp···cu··· a···ne···l ···wo···dy···ic···ta···da···g ···or···m ··· s···du···g ···or···m ···da···la···an···ey···ch···ue···f ··· s···la···n ···vi···al···se···vi···n.

项目受资助省

江苏·

  • 排序方式:
  • 5
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  • 3.受昆···系统···图像···法(···

    • 《智能···报》
    • 2009年
    • 02期
    • 期刊

    虽然···素重···迭代···算法···TO···模型···提出···该方···大量···次数···在噪···效果···待于···因此···则化···反投···被提···系统···像.···适应···差正···子和···变差···因子···化迭···影算···适应···正则···根据···当前···选择···因此···子正···的迭···影算···在平···的同···高频···而双···差正···子是···素点···近领···邻近···判别···否为···,考···多的···息,···以跨···平滑···同时···变差···因子···大地···构的···实验···在仿···图像···验结···了这···则化···算法···性.

    ...
  • 4.Cu···le···叶斯···扫声···降斑··

    • 关键词:
    • 声呐图像;降斑;贝叶斯估计;Curvelet;局部自适应
    • 霍冠···庆武···;范···范新·
    • 《仪器···报》
    • 2011年
    • 01期
    • 期刊

    针对···呐图···点噪···出了···叶斯···Cu···le···斑方···据海···模型···侧扫···像斑···的瑞···乘性···型。···数后···图像···ur···et···依据···数的···布、···数的···布,···叶斯···最大···率估···,在···件下···Cu···le···域系···的理···式。···部自···邻域···定方···Cu···le···域处···系数···变换···过指···后得···的侧···图像···结果···在客···指标···视觉···面,···均取···于传···间滤···于小···斑方···果。

    ...
  • 5.基于···价系···代图···方法

    • 关键词:
    • 图像融合;加权平均;塔型变换;客观评价法
    • 张学···小艳···业;···;范··
    • 《仪器···报》
    • 2010年
    • 09期
    • 期刊

    本文···统的···均法···塔型···多尺···方法···方根···平均···否为···判别···提出···客观···数的···合方···先将···的源···行传···的融···较融···像的···价系···定阈···,倘···设定···则融···结束···价系···设定···则继···融合···进行···合结···观评···达到···。实···表明···法的···果相···统方···精确·

    ...
  • 7.优化···求解···总变···复原

    • 关键词:
    • 总变分;广义总变分;优化-最小算法;图像复原;反问题;双边滤波
    • 徐梦···枫;···;李·
    • 《中国···形学·》
    • 2011年
    • 07期
    • 期刊

    在代···中嵌···分正···解决···原中···问题···有效···但是···分正···虑的···阶而···阶邻···变分···另外···分的···式还···总变···函数···带来···。为···出一···优化···算法···总变···化图···新方···克服···在的···该方···了总···则化···够除···边缘···特征···借鉴···总变···加权···从而···总变···项在···围上···形式···法还···义总···则项···程中···求解···提出···化-···法求···函数···逼近···。实···表明···法取···好的···果,···信噪···达到···B左··

    ...
  • 8.联合···变换···P估···感图···

    • 关键词:
    • 图像融合;小波变换;MAP;IHS变换
    • 石爱···立中···
    • 《遥感··》
    • 2010年
    • 06期
    • 期刊

    为了···光谱···全色···融合···提出···于推···HS···ne···iz···In···si···Hu···at···ti···GI···变换···后验···AP···xi···a ···te···ri···合的···像融···。该···先经···HS···由多···像得···分量···针对···量和···像,···AP···分辨···的成···,采···下降···法得···光谱···高分···色图···而依···HS···到融···。实···别以···NO···、Q···kb···卫星···谱图···色图···,进···算法···并与···S融···、传···波变···算法···变换···HS···融合···进行···析,···明,···合方···更好···效果·

    ...
  • 10.A ··· F···ew··· o···or···iz···Co···lu···n ··· S···rr···lu···n ···ng···bu···Ce···in··

    • 关键词:
    • IMAGE; REGISTRATION
    • Xu···en···an···Hu···n;··· L···on···ua··· C···ro··
    • 《20···2N···NT···AT···AL···NF···NC···N ···PU··· A···AU···AT··· E···NE···NG···CC···20···, ··· 2》
    • 2010年
    • 期刊

    In···e ···ce···of···co···ng···di···al···ag···su···-r···lu···n ···) ···a ···si··· s··· m···od···r ···vi···th···im···ti···of···vi···an···ff··· o···nv···nm···. ···in···as···wo···ca···, ···y ···ea···er···ro···ed···ri··· S···lg···th···fo···ma···re···st···ti··· A···g ···se···go···hm···no···li··· c···ol···on···C)··· a···om···ng···pr···h ···ch···ns···rs···t ···y ···ti···di···nc···et···n ···te···ix···an···ei···or···xe···ut···so···si···l ···or···tw··· p···l'···ea···em··· a···es···at···. ···ev··· t···pr···em··· r···vi···no··· a···ou···er··· t···it···al··· c···be···rt··· s···ie···nd···lv··· I···hi···ap··· w···ro···es···ne···ra···or···f ··· B···de···bo···fa···rs···ns···re···th···ew··· u···g ···a ···bi···er···fi···r ···si···s ···it···al···ct··· p···om···ic···ff···nc···et···n ···gh··· p···ls···o ···ca···bt··· m··· a···ra···re···t.··· t···ot··· h···, ···s ···er···gg···s ···ob··· c···ai··· f···ti···in··· b···d ···tw···ra···io··· c···ai···es··· d···ct···d ···ov···or···ut···rs···xp···me··· a···ca···ed···t ···de···st···e ··· e···ct···ne···of···r ···ho··

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