matlab中存档算法代码-Blind-deconvolution:盲反卷积

上传者: 38665162 | 上传时间: 2021-08-01 17:06:07 | 文件大小: 291.65MB | 文件类型: ZIP
matlab中存档算法代码盲反卷积 盲反卷积是使用未知模糊内核对图像进行模糊处理的过程。 我的大部分工作都与Rob Fergus的相关工作及其实施有关 为了提取清晰的图像,我们首先需要计算模糊核。 后者是使用最大后验(MAP)算法估算的,同时假设模糊核值具有指数先验分布。 理想情况下,先计算后验分布,然后再使用MAP算法。 在估计了模糊内核之后,使用Richardson Lucy算法(非盲反卷积)算法来获取最终锐化图像的像素值。 我的文章中给出了该算法的详细解释。 结果很少显示如下: 您还可以通过仅选择特定的图像区域并将其作为算法的输入,来锐化图像的一部分。 例如,考虑下面的模糊图像及其结果。 在这里,我只是想使瓶子更锋利,而不是使backgorund变得更锋利。 在任何模糊图像上运行代码的步骤: 将模糊的图像复制到images /中(例如ian1.jpg) 复制结果/中的示例图像脚本之一(例如,如果使用Linux,则为“ cp ian1.m ian1.m”) 编辑新的图像脚本(例如ian1.m),更改以下设置:-obs_im以反映新的文件名(例如obs_im ='../images/

文件下载

资源详情

[{"title":"( 81 个子文件 291.65MB ) matlab中存档算法代码-Blind-deconvolution:盲反卷积","children":[{"title":"Blind-deconvolution-master","children":[{"title":"images","children":[{"title":"tiger.jpg <span style='color:#111;'> 325.04KB </span>","children":null,"spread":false},{"title":"family.jpg <span style='color:#111;'> 97.90KB </span>","children":null,"spread":false},{"title":"bottle.jpg <span style='color:#111;'> 401.53KB </span>","children":null,"spread":false},{"title":"ian1.jpg <span style='color:#111;'> 431.37KB </span>","children":null,"spread":false},{"title":"motion0050.jpg <span style='color:#111;'> 96.78KB </span>","children":null,"spread":false},{"title":"lyndsey2.jpg <span style='color:#111;'> 833.96KB </span>","children":null,"spread":false}],"spread":true},{"title":"deconvolution","children":[{"title":"deconWiener.m <span style='color:#111;'> 360B </span>","children":null,"spread":false},{"title":"deconBlind.m <span style='color:#111;'> 309B </span>","children":null,"spread":false},{"title":"blurring.m <span style='color:#111;'> 94B </span>","children":null,"spread":false},{"title":"iandecon_blind.png <span style='color:#111;'> 274.40KB </span>","children":null,"spread":false},{"title":"bottledecon_blind.png <span style='color:#111;'> 579.58KB </span>","children":null,"spread":false},{"title":"familydecon_blind.png <span style='color:#111;'> 172.33KB </span>","children":null,"spread":false}],"spread":true},{"title":"noising_and_saving_images.ipynb <span style='color:#111;'> 209.71KB </span>","children":null,"spread":false},{"title":"priors","children":[{"title":"linear_street_4.mat <span style='color:#111;'> 1.59KB </span>","children":null,"spread":false},{"title":"linear_whiteboard_4.mat <span style='color:#111;'> 1.59KB </span>","children":null,"spread":false}],"spread":true},{"title":"results","children":[{"title":"family.m <span style='color:#111;'> 6.69KB </span>","children":null,"spread":false},{"title":"lyndsey2_kernel.eps <span style='color:#111;'> 8.53KB </span>","children":null,"spread":false},{"title":"motion0050.m <span style='color:#111;'> 6.56KB </span>","children":null,"spread":false},{"title":"motion0050_kernel.eps <span style='color:#111;'> 8.53KB </span>","children":null,"spread":false},{"title":"family_kernel.eps <span style='color:#111;'> 8.86KB </span>","children":null,"spread":false},{"title":"ian1_kernel.eps <span style='color:#111;'> 8.86KB </span>","children":null,"spread":false},{"title":"ian1.m <span style='color:#111;'> 6.61KB </span>","children":null,"spread":false},{"title":"lyndsey2.m <span style='color:#111;'> 5.95KB </span>","children":null,"spread":false},{"title":"best_results","children":[{"title":"iankernel_best.png <span style='color:#111;'> 22.93KB </span>","children":null,"spread":false},{"title":"ian1_best.jpg <span style='color:#111;'> 434.09KB </span>","children":null,"spread":false},{"title":"familykernel_k_35_it_20_num_scales_7_reg_1.png <span style='color:#111;'> 23.92KB </span>","children":null,"spread":false},{"title":"bottle_best.png <span style='color:#111;'> 229.98KB </span>","children":null,"spread":false},{"title":"ian_best.png <span style='color:#111;'> 977.24KB </span>","children":null,"spread":false},{"title":"family_k_35_it_20_num_scales_7_reg_1.png <span style='color:#111;'> 1.12MB </span>","children":null,"spread":false},{"title":"bottle_ker_k30.PNG <span style='color:#111;'> 26.73KB </span>","children":null,"spread":false}],"spread":false},{"title":"lyndsey2_final.jpg <span style='color:#111;'> 1.11MB </span>","children":null,"spread":false},{"title":"ian1_final.jpg <span style='color:#111;'> 434.09KB </span>","children":null,"spread":false},{"title":"bottle_kernel.eps <span style='color:#111;'> 8.86KB </span>","children":null,"spread":false},{"title":"bottle.m <span style='color:#111;'> 5.96KB </span>","children":null,"spread":false}],"spread":false},{"title":"README <span style='color:#111;'> 30.41KB </span>","children":null,"spread":false},{"title":"code","children":[{"title":"reconsEdge3.m <span style='color:#111;'> 656B </span>","children":null,"spread":false},{"title":"train_ensemble_get.m <span style='color:#111;'> 1.74KB </span>","children":null,"spread":false},{"title":"train_blind_deconv.m <span style='color:#111;'> 4.30KB </span>","children":null,"spread":false},{"title":"train_ensemble_put.m <span style='color:#111;'> 1.77KB </span>","children":null,"spread":false},{"title":"tiger.mat <span style='color:#111;'> 32.06MB </span>","children":null,"spread":false},{"title":"randnND.m <span style='color:#111;'> 507B </span>","children":null,"spread":false},{"title":"train_ensemble_evidence6.m <span style='color:#111;'> 12.15KB </span>","children":null,"spread":false},{"title":"tmp_ian1.mat <span style='color:#111;'> 12.66MB </span>","children":null,"spread":false},{"title":"ultimateSubplot.m <span style='color:#111;'> 533B </span>","children":null,"spread":false},{"title":"deblur.m <span style='color:#111;'> 28.18KB </span>","children":null,"spread":false},{"title":"tmp_bottle.mat <span style='color:#111;'> 29.82MB </span>","children":null,"spread":false},{"title":"greenspan.m <span style='color:#111;'> 1.06KB </span>","children":null,"spread":false},{"title":"fix_image.m <span style='color:#111;'> 869B </span>","children":null,"spread":false},{"title":"train_ensemble_rectified5.m <span style='color:#111;'> 3.49KB </span>","children":null,"spread":false},{"title":"fiddle_lucy4.m <span style='color:#111;'> 5.15KB </span>","children":null,"spread":false},{"title":"tmp_family.mat <span style='color:#111;'> 37.83MB </span>","children":null,"spread":false},{"title":"deconvlucy_intens.m <span style='color:#111;'> 335B </span>","children":null,"spread":false},{"title":"invDel2.m <span style='color:#111;'> 545B </span>","children":null,"spread":false},{"title":"motion0050.mat <span style='color:#111;'> 11.37MB </span>","children":null,"spread":false},{"title":"normMDpdf.m <span style='color:#111;'> 571B </span>","children":null,"spread":false},{"title":"fiddle_lucy3.m <span style='color:#111;'> 4.90KB </span>","children":null,"spread":false},{"title":"histmatch.m <span style='color:#111;'> 1.52KB </span>","children":null,"spread":false},{"title":"ExportFig.m <span style='color:#111;'> 1.44KB </span>","children":null,"spread":false},{"title":"estimate_priors2.m <span style='color:#111;'> 2.80KB </span>","children":null,"spread":false},{"title":"plotgray.m <span style='color:#111;'> 3.75KB </span>","children":null,"spread":false},{"title":"train_ensemble_put_lambda.m <span style='color:#111;'> 1.82KB </span>","children":null,"spread":false},{"title":"delta_kernel.m <span style='color:#111;'> 358B </span>","children":null,"spread":false},{"title":"rgb2gray_rob.m <span style='color:#111;'> 2.16KB </span>","children":null,"spread":false},{"title":"initialize_parameters2.m <span style='color:#111;'> 5.70KB </span>","children":null,"spread":false},{"title":"tmp_tiger.mat <span style='color:#111;'> 12.60MB </span>","children":null,"spread":false},{"title":"ian1.mat <span style='color:#111;'> 19.72MB </span>","children":null,"spread":false},{"title":"mix_exponentials.m <span style='color:#111;'> 495B </span>","children":null,"spread":false},{"title":"clip_image.m <span style='color:#111;'> 556B </span>","children":null,"spread":false},{"title":"automatic_patch_selector.m <span style='color:#111;'> 1.83KB </span>","children":null,"spread":false},{"title":"prefZeros.m <span style='color:#111;'> 558B </span>","children":null,"spread":false},{"title":"move_level.m <span style='color:#111;'> 2.58KB </span>","children":null,"spread":false},{"title":"family.mat <span style='color:#111;'> 50.33MB </span>","children":null,"spread":false},{"title":"train_ensemble_main6.m <span style='color:#111;'> 13.14KB </span>","children":null,"spread":false},{"title":"GaussianMixtures1D.m <span style='color:#111;'> 5.55KB </span>","children":null,"spread":false},{"title":"lyndsey2.mat <span style='color:#111;'> 15.72MB </span>","children":null,"spread":false},{"title":"bottle.mat <span style='color:#111;'> 47.81MB </span>","children":null,"spread":false},{"title":"tmp_lyndsey2.mat <span style='color:#111;'> 7.65MB </span>","children":null,"spread":false},{"title":"train_ensemble_get_lambda.m <span style='color:#111;'> 1.79KB </span>","children":null,"spread":false},{"title":"create_greenspan_settings.m <span style='color:#111;'> 2.47KB </span>","children":null,"spread":false},{"title":"tmp_motion0050.mat <span style='color:#111;'> 6.70MB </span>","children":null,"spread":false}],"spread":false},{"title":"README.md <span style='color:#111;'> 3.93KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明