基于特征的图像修复
如果依赖于图像的可用区域,试图修复一幅图像中大片的缺失区域,结果可能不会很真实. 利用对齐图像的具有代表性的数据集,可以使用多幅图像对采用 FeatureExtraction 的修复函数进行训练.
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H9r6bfvbB+wGOq/ZX+saXDh4esh56Nx1j+sXb/jeuHpzyc3BW+G37tyOvT18
h3tn4m7a3Rf3su/N3l/3APug+KHMw8pHKo/qfjT4sXXYdvjUiMdI3+PQx/dH
OaPPfsr66cNY4RPqk8px9fHGCcuJk5Nek9eeLn069izz2exU0c+yP+95rv/8
u19cfumbjpoeeyF8Mfdr6Uull4deWb/qngmaefQ6/fXsm+K3Sm8Pv2O9630f
+X58NucD/kPVR4OPXZ/8Pj2YS5+b+w0AsgC3
"], "RGB", "XYZ"],
Interleaving -> True, MetaInformation -> {}],
Selectable->False],
BaseStyle->"ImageGraphics",
ImageSizeRaw->{300, 200},
PlotRange->{{0, 300}, {0, 200}}]\)};
missingparts = {\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
1:eJzt2LFqFFEUgOG9Vpa+gr0PYGtpG/EBEozBJkIiiG/oYxlJ4zrZ9T/VbuT7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"], {{0, 200}, {300, 0}}, {0, 1},
ColorFunction->GrayLevel],
BoxForm`ImageTag["Bit", ColorSpace -> Automatic, Interleaving -> None],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{300, 200},
PlotRange->{{0, 300}, {0, 200}}]\), None, \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
1:eJzt2zFqm0EUhdFfqVx6C+6zALcu0yp4ATaRTRoZpIDxDrOsSIWFYmTrXjf+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"], {{0, 200}, {300, 0}}, {0, 1},
ColorFunction->GrayLevel],
BoxForm`ImageTag["Bit", ColorSpace -> Automatic, Interleaving -> None],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{300, 200},
PlotRange->{{0, 300}, {0, 200}}]\), None, None};
可以通过图像数据集中像素值的协变导出有可能用来预测缺失区域的统计特征.
显示完整的 Wolfram 语言输入
In[3]:=
inpaintFromList[dataset_, masks_, n_] := Module[
{data, fe},
data = prepareTrueAndMissingData[dataset, masks];
fe = FeatureExtraction[data, "MissingImputed"];
Image[ArrayReshape[fe[data[[n]]],
Reverse@ImageDimensions@dataset[[1]]]]
];
对第三幅图像的每个颜色通道分别进行基于特征的修复.
In[4]:=
result = ColorCombine[
Table[
Image[inpaintFromList[channels, missingparts, 3],
"Byte"], {channels, Transpose[ColorSeparate /@ images]}]]
Out[4]=
突出显示修复区域.
In[5]:=
HighlightImage[result, {FaceForm[],
EdgeForm[{Dashed, AbsoluteThickness[1], White}], missingparts[[3]]},
ImageSize -> Medium]
Out[5]=
与只使用输入图像进行修补的 Inpaint 的结果进行比较.
In[6]:=
HighlightImage[
Inpaint[images[[3]], missingparts[[3]]], {FaceForm[],
EdgeForm[{Dashed, AbsoluteThickness[1], White}], missingparts[[3]]},
ImageSize -> Medium]
Out[6]=