FastNLMeans¶
将NL表示滤镜应用于图像。
描述¶
实现是NL方法的近似值，其速度更快。
参数¶
Input image -in image Mandatory
Input image to denoise
Output Image -out image [dtype] Mandatory
Output image.
Patch radius (patch is a square) -patchradius int Default value: 2
Full patch will have a size of 2*patchradius +1.
Search window radius (search window is a square) -searchradius int Default value: 7
Search window is used to find similar patches. Its size will be 2*searchradius+1.
Standard deviation in image -sig float Default value: 0
Noise standard deviation estimated in image. This parameter is used to correct for the expected difference between two patches. This filter works fine without using this tuning.
Similarity threshold -thresh float Default value: 1
Factor influencing similarity score of two patches. The higher the threshold, the more permissive the filter. It is common to set this threshold slightly below the standard deviation (for Gaussian noise), at about 0.8*sigma.
Available RAM (MB) -ram int Default value: 256
Available memory for processing (in MB).
实例¶
从命令行执行以下操作：
otbcli_FastNLMeans -in GomaAvant.tif -out denoisedImage_NLMeans.tif
来自Python的评论：
import otbApplication
app = otbApplication.Registry.CreateApplication("FastNLMeans")
app.SetParameterString("in", "GomaAvant.tif")
app.SetParameterString("out", "denoisedImage_NLMeans.tif")
app.ExecuteAndWriteOutput()
局限性¶
该滤镜依赖于积分图像。虽然OTB机制以块为单位处理数据，但风险有限，但仍有可能发生溢出。