<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Ahmed Ben Said, Rachid Hadjidj, Kamal Eddine Melkemi, Sebti Foufou: Multispectral image denoising with optimized vector non-local mean filter</style></title><secondary-title><style face="normal" font="default" size="100%">Digital Signal Processing, Elsevier</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/abs/pii/S1051200416301099</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">58</style></volume><pages><style face="normal" font="default" size="100%">115-126</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;span style=&quot;display:inline!important;float:none;background-color:rgb(255,255,255);color:rgb(46,46,46);nexusserif,georgia,timesnewroman,times,stixgeneral,cambriamath,lucidasansunicode,microsoftsansserif,segoeuisymbol,arialunicodems,serif;18px;font-style:normal;font-variant:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:left;text-decoration:none;text-indent:0px;text-transform:none;-webkit-text-stroke-width:0px;white-space:normal;word-spacing:0px;&quot;&gt;Nowadays, many applications rely on images of high quality to ensure good performance in conducting their tasks. However, noise goes against this objective as it is an unavoidable issue in most applications. Therefore, it is essential to develop techniques to attenuate the impact of noise, while maintaining the integrity of relevant information in images. We propose in this work to extend the application of the Non-Local Means filter (NLM) to the vector case and apply it for denoising &lt;/span&gt;&lt;a title=&quot;Learn more about Multispectral Image from ScienceDirect's AI-generated Topic Pages&quot; style=&quot;background-color:transparent;box-sizing:border-box;color:rgb(12,125,187);nexusserif,georgia,timesnewroman,times,stixgeneral,cambriamath,lucidasansunicode,microsoftsansserif,segoeuisymbol,arialunicodems,serif;18px;font-style:normal;font-variant:normal;font-weight:400;letter-spacing:normal;margin-bottom:0px;margin-left:0px;margin-right:0px;margin-top:0px;orphans:2;padding-bottom:0px;padding-left:0px;padding-right:0px;padding-top:0px;text-align:left;text-decoration:none;text-indent:0px;text-transform:none;-webkit-text-stroke-width:0px;white-space:normal;word-spacing:0px;&quot; href=&quot;https://www.sciencedirect.com/topics/engineering/multispectral-image&quot;&gt;multispectral images&lt;/a&gt;&lt;span style=&quot;box-sizing:border-box;color:rgb(46,46,46);nexusserif,georgia,timesnewroman,times,stixgeneral,cambriamath,lucidasansunicode,microsoftsansserif,segoeuisymbol,arialunicodems,serif;18px;font-style:normal;font-variant:normal;font-weight:400;letter-spacing:normal;margin-bottom:0px;margin-left:0px;margin-right:0px;margin-top:0px;orphans:2;padding-bottom:0px;padding-left:0px;padding-right:0px;padding-top:0px;text-align:left;text-decoration:none;text-indent:0px;text-transform:none;-webkit-text-stroke-width:0px;white-space:normal;word-spacing:0px;&quot;&gt;&lt;span style=&quot;box-sizing:border-box;margin-bottom:0px;margin-left:0px;margin-right:0px;margin-top:0px;padding-bottom:0px;padding-left:0px;padding-right:0px;padding-top:0px;&quot;&gt;. The objective is to benefit from the additional information brought by multispectral &lt;a title=&quot;Learn more about Imaging Systems from ScienceDirect's AI-generated Topic Pages&quot; style=&quot;background-color:transparent;box-sizing:border-box;color:rgb(12,125,187);margin-bottom:0px;margin-left:0px;margin-right:0px;margin-top:0px;padding-bottom:0px;padding-left:0px;padding-right:0px;padding-top:0px;text-decoration:none;&quot; href=&quot;https://www.sciencedirect.com/topics/computer-science/imaging-systems&quot;&gt;imaging systems&lt;/a&gt;. The NLM filter exploits the redundancy of information in an image to remove noise. A restored pixel is a weighted average of all pixels in the image. In our contribution, we propose an optimization framework where we dynamically fine tune the NLM filter parameters and attenuate its computational complexity by considering only pixels which are most similar to each other in computing a restored pixel. Filter parameters are optimized using Stein's Unbiased Risk Estimator (SURE) rather than using ad hoc means. Experiments have been conducted on multispectral images corrupted with &lt;/span&gt;&lt;a title=&quot;Learn more about Additive White Gaussian Noise from ScienceDirect's AI-generated Topic Pages&quot; style=&quot;background-color:transparent;box-sizing:border-box;color:rgb(12,125,187);margin-bottom:0px;margin-left:0px;margin-right:0px;margin-top:0px;padding-bottom:0px;padding-left:0px;padding-right:0px;padding-top:0px;text-decoration:none;&quot; href=&quot;https://www.sciencedirect.com/topics/engineering/additive-white-gaussian-noise&quot;&gt;additive white Gaussian noise&lt;/a&gt;. PSNR and similarity comparison with other approaches are provided to illustrate the efficiency of our approach in terms of both denoising performance and computation complexity.&lt;/span&gt;</style></abstract></record></records></xml>