US20130163895A1 - Image processing for removing a noise component contained in an image - Google Patents
US20130163895A1
US20130163895A1 US13/731,736 US201213731736A US2013163895A1 US 20130163895 A1 US20130163895 A1 US 20130163895A1 US 201213731736 A US201213731736 A US 201213731736A US 2013163895 A1 US2013163895 A1 US 2013163895A1
US13/731,736
US8675982B2 (en
2007-02-02 Priority to JP2007024066 priority Critical
2007-02-02 Priority to JP2007-024067 priority
2007-02-02 Priority to JP2007024067 priority
2007-02-02 Priority to JP2007-024066 priority
2008-02-01 Priority to PCT/JP2008/051652 priority patent/WO2008093836A1/en
2009-07-24 Priority to US12/458,855 priority patent/US8369640B2/en
2012-12-31 Priority to US13/731,736 priority patent/US8675982B2/en
2012-12-31 Application filed by Nikon Corp filed Critical Nikon Corp
2013-06-27 Publication of US20130163895A1 publication Critical patent/US20130163895A1/en
2014-03-18 Publication of US8675982B2 publication Critical patent/US8675982B2/en
V ij({right arrow over (x)})=Wavelet(i,j) {S({right arrow over (x)})},j=LL,LH,HL,HH i=1, 2, . . . , 5 (resolution) [Expression 6]
It is to be noted that a subband image Vij (x, y) is generated with S(x, y) in correspondence to each of the L̂âb̂ planes.
It is also to be noted that in the wavelet transformation, through which an image (image data) is converted to frequency component data, the frequency component in the image is divided into a high-pass component and a low-pass component. Data made up of the high-pass component referred to as a high-frequency subband, whereas data made up of the low-pass component are referred to as a low-frequency subband. The LL component data are low-frequency subband data, whereas the LH, HL and HH data are high-frequency subband data. Also, a low-frequency subband may be referred to as a low-frequency image and a high-frequency subband may be referred to as a high-frequency image. Furthermore, each subband may be referred to as a frequencyband-limited image. A low-frequency subband is an image with band limits imposed upon the frequency band of the original image toward the low-frequency side, whereas a high-frequency subband is an image with band limits imposed upon the frequency band of the original image toward the high-frequency side.
x[2n+1=d[n]+(x[2n+2]+x[2n])/2
 [ Expression   7 ] V ′  ( x → ) = ∫  x → ′ - x →  ≤ 2  r th  V  ( x → ′ )  exp  ( -  V  ( x → ′ ) - V  ( x → )  2 σ th 2 ·  x → ′ - x →  2 r th 2 )    x → ′ ∫  x → ′ - x →  ≤ 2  r th  exp  ( -  V  ( x → ′ ) - V  ( x → )  2 σ th 2 ·  x → ′ - x →  2 r th 2 )    x → ′
N  ( x → ) = ∑ i = LL , LH , HL , HH j = 1 , 2 ,  …  , 5   Wavelet - 1  { n ij  ( x → ) } [ Expression   9 ]
N M  ( x → ) = ∑ i = LL , LH , HL , HH j = 5 , 4 ,  …  , M + 1   Wavelet - 1  { n ij  ( x → ) } [ Expression   10 ]
When M=5, the noise component synthesis is executed with noise actually not extracted and thus, N5 (x, y)=0.
∇ 2  =  1 - ( Gaussian ) 9 × 9 =  1  - t  ( 1 8 28 56 70 56 28 8 1 ) / 256 ⊗  ( 1 8 28 56 70 56 28 8 1 ) / 256 [ Expression   13 ]
n ij ′  ( x -> ) = n ij  ( x -> ) · exp  ( - e ij  ( x _ ) 2 σ ethij 2 ) [ Expression   14 ]
N w  ( x _ ) = ∑ i = LL , LH , HL , HH j = 1 , 2 ,  …  , 5  Wavelet - 1  { k nij · n ij ′  ( x -> ) } [ Expression   15 ]
N w  ( x -> ) = ∑ i = LL , LH , HL , HH j = 1 , 2 ,  …  , 5  Wavelet - 1  { k nij · n ij ′  ( x _ ) } [ Expression   17 ]
E w  ( x -> ) = ∑ i = LL , LH , HL , HH j = 1 , 2 ,  …  , 5  Wavelet - 1  { k eij · e ij  ( x -> ) } [ Expression   18 ]
N w ′  ( x -> ) = N w  ( x -> ) · exp ( - E w  ( x -> ) 2 σ Eth 2 ) [ Expression   19 ]
n ij ′  ( x _ ) = n ij  ( x -> ) · exp ( - e ij  ( x -> ) 2 σ ethij 2 ) [ Expression   22 ]
N w  ( x _ ) = ∑ i = LL , LH , HL , HH j = 1 , 2 ,  …  , 5  Wavelet - 1  { k nij · n ij ′  ( x -> ) } [ Expression   23 ]
E w  ( x -> ) = ∑ i = LL , LH , HL , HH j = 1 , 2 ,  …  , 5  Wavelet - 1  { k eij · e ij  ( x -> ) } [ Expression   24 ]
λ  ( x -> ) = λ 0 · exp ( - E w  ( x -> ) 2 σ Eth 2 ) exp  ( - α 2 σ Eth 2 )   λ   0 = const .  α = const . [ Expression   25 ]
S ′  ( x -> ) = ∫  x -> ′ - x _  ≤ 2  r th  S  ( x -> ′ )  exp  ( -  S  ( x _ ′ ) - S  ( x -> )  2 σ th 2 ·  x -> ′ - x _  2 r th 2 )   x _ ′ ∫  x -> ′ - x _  ≤ 2  r th  exp  ( -  S  ( x -> ′ ) - S  ( x -> )  2 σ th 2 ·  x -> ′ - x _  2 r th 2 )   x -> ′ [ Expression   28 ]
∇ 2  =  1 - ( Gaussian ) 25 × 25 =  1 - ( Gaussian ) 9 × 9 ⊗ ( Gaussian ) 9 × 9 ⊗ ( Gaussian ) 9 × 9 [ Expression   31 ]
λ  ( x _ ) = λ 0 · exp  ( - E  ( x _ ) 2 σ Eth 2 ) exp  ( - α 2 σ Eth 2 )   λ   0 = const .  α = const . [ Expression   32 ]
 V ′  ( x -> ) = V  ( x -> ) - ∇ 2  V  ( x _ ) · exp ( -  ∇ 2  V  ( x _ )  2 σ th 2 )    V ″  ( x -> ) = V ′  ( x -> ) + ∇ 2  V ′  ( x -> ) · exp ( -  ∇ 2  V ′  ( x -> )  2 σ th 2 )    V ′′′  ( x _ ) = V ″  ( x -> ) - ∇ 2  V ″  ( x -> ) · exp ( -  ∇ 2  V ″  ( x _ )  2 σ th 2 )   V ( 4 )  ( x _ ) = V ′′′  ( x _ ) + ∇ 2  V ′′′  ( x -> ) · exp ( -  ∇ 2  V ′′′  ( x -> )  2 σ th 2 ) [ Expression   35 ]
V ′  ( x _ ) = V  ( x _ ) - ∇ 2  V  ( x -> ) · exp ( -  ∇ 2  V  ( x -> )  2 σ th 2 )   V ″  ( x _ ) = V ′  ( x -> ) - ∇ 2  V ′  ( x -> ) · exp ( -  ∇ 2  V ′  ( x _ )  2 σ th 2 ) [ Expression   36 ]
∇ 2  =  1 - ( Gaussian ) 9 × 9 =  1 - ′  ( 1 8 28 56 70 56 28 8 1 ) 256 ⊗ ( 1 8 28 56 70 56 28 8 1 ) 256 [ Expression   37 ]
a quantity of an edge component present in the extracted noise component is estimated based upon a level of the synthesized edge component
10. An image processing method according to claim 7, wherein:
US13/731,736 2007-02-02 2012-12-31 Image processing for removing a noise component contained in an image Active US8675982B2 (en)
US12/458,855 US8369640B2 (en) 2007-02-02 2009-07-24 Image processing for removing a noise component contained in an image
US13/731,736 US8675982B2 (en) 2007-02-02 2012-12-31 Image processing for removing a noise component contained in an image
US12/458,855 Division US8369640B2 (en) 2007-02-02 2009-07-24 Image processing for removing a noise component contained in an image
US8675982B2 US8675982B2 (en) 2014-03-18
US12/458,855 Active 2030-02-16 US8369640B2 (en) 2007-02-02 2009-07-24 Image processing for removing a noise component contained in an image
US13/731,736 Active US8675982B2 (en) 2007-02-02 2012-12-31 Image processing for removing a noise component contained in an image
TW201318418A (en) * 2011-10-26 2013-05-01 Novatek Microelectronics Corp Method of noise reduction in image and device thereof
2008-02-01 WO PCT/JP2008/051652 patent/WO2008093836A1/en active Application Filing
2008-02-01 JP JP2008556197A patent/JP4766118B2/en active Active
2009-07-24 US US12/458,855 patent/US8369640B2/en active Active
2012-12-31 US US13/731,736 patent/US8675982B2/en active Active
JPWO2008093836A1 (en) 2010-05-20
US8675982B2 (en) 2014-03-18
WO2008093836A1 (en) 2008-08-07
US20090290067A1 (en) 2009-11-26
JP4766118B2 (en) 2011-09-07
US8369640B2 (en) 2013-02-05
JP4711962B2 (en) 2011-06-29 Method and apparatus for modeling a pattern of film grain in the frequency domain
KR100754181B1 (en) 2007-09-03 Method and apparatus for reducing mosquito noise in fecoded video sequence
JP2005117549A (en) 2005-04-28 Image processing method, image processor, and image processing program
JPWO2004112401A1 (en) 2006-07-20 Image processing method, image processing program, the image processing apparatus
JP2001229377A (en) 2001-08-24 Method for adjusting contrast of digital image by adaptive recursive filter
JPWO2002093935A1 (en) 2004-09-02 Image processing apparatus
US20130163895A1 - Image processing for removing a noise component contained in an image - Google Patents
US20130163895A1
US20130163895A1 US13/731,736 US201213731736A US2013163895A1 US 20130163895 A1 US20130163895 A1 US 20130163895A1 US 201213731736 A US201213731736 A US 201213731736A US 2013163895 A1 US2013163895 A1 US 2013163895A1
US13/731,736
US8675982B2 (en
2007-02-02 Priority to JP2007024066 priority Critical
2007-02-02 Priority to JP2007-024067 priority
2007-02-02 Priority to JP2007024067 priority
2007-02-02 Priority to JP2007-024066 priority
2008-02-01 Priority to PCT/JP2008/051652 priority patent/WO2008093836A1/en
2009-07-24 Priority to US12/458,855 priority patent/US8369640B2/en
2012-12-31 Priority to US13/731,736 priority patent/US8675982B2/en
2012-12-31 Application filed by Nikon Corp filed Critical Nikon Corp
2013-06-27 Publication of US20130163895A1 publication Critical patent/US20130163895A1/en
2014-03-18 Publication of US8675982B2 publication Critical patent/US8675982B2/en
V ij({right arrow over (x)})=Wavelet(i,j) {S({right arrow over (x)})},j=LL,LH,HL,HH i=1, 2, . . . , 5 (resolution) [Expression 6]
It is to be noted that a subband image Vij (x, y) is generated with S(x, y) in correspondence to each of the L̂âb̂ planes.
It is also to be noted that in the wavelet transformation, through which an image (image data) is converted to frequency component data, the frequency component in the image is divided into a high-pass component and a low-pass component. Data made up of the high-pass component referred to as a high-frequency subband, whereas data made up of the low-pass component are referred to as a low-frequency subband. The LL component data are low-frequency subband data, whereas the LH, HL and HH data are high-frequency subband data. Also, a low-frequency subband may be referred to as a low-frequency image and a high-frequency subband may be referred to as a high-frequency image. Furthermore, each subband may be referred to as a frequencyband-limited image. A low-frequency subband is an image with band limits imposed upon the frequency band of the original image toward the low-frequency side, whereas a high-frequency subband is an image with band limits imposed upon the frequency band of the original image toward the high-frequency side.
x[2n+1=d[n]+(x[2n+2]+x[2n])/2
 [ Expression   7 ] V ′  ( x → ) = ∫  x → ′ - x →  ≤ 2  r th  V  ( x → ′ )  exp  ( -  V  ( x → ′ ) - V  ( x → )  2 σ th 2 ·  x → ′ - x →  2 r th 2 )    x → ′ ∫  x → ′ - x →  ≤ 2  r th  exp  ( -  V  ( x → ′ ) - V  ( x → )  2 σ th 2 ·  x → ′ - x →  2 r th 2 )    x → ′
N  ( x → ) = ∑ i = LL , LH , HL , HH j = 1 , 2 ,  …  , 5   Wavelet - 1  { n ij  ( x → ) } [ Expression   9 ]
N M  ( x → ) = ∑ i = LL , LH , HL , HH j = 5 , 4 ,  …  , M + 1   Wavelet - 1  { n ij  ( x → ) } [ Expression   10 ]
When M=5, the noise component synthesis is executed with noise actually not extracted and thus, N5 (x, y)=0.
∇ 2  =  1 - ( Gaussian ) 9 × 9 =  1  - t  ( 1 8 28 56 70 56 28 8 1 ) / 256 ⊗  ( 1 8 28 56 70 56 28 8 1 ) / 256 [ Expression   13 ]
n ij ′  ( x -> ) = n ij  ( x -> ) · exp  ( - e ij  ( x _ ) 2 σ ethij 2 ) [ Expression   14 ]
N w  ( x _ ) = ∑ i = LL , LH , HL , HH j = 1 , 2 ,  …  , 5  Wavelet - 1  { k nij · n ij ′  ( x -> ) } [ Expression   15 ]
N w  ( x -> ) = ∑ i = LL , LH , HL , HH j = 1 , 2 ,  …  , 5  Wavelet - 1  { k nij · n ij ′  ( x _ ) } [ Expression   17 ]
E w  ( x -> ) = ∑ i = LL , LH , HL , HH j = 1 , 2 ,  …  , 5  Wavelet - 1  { k eij · e ij  ( x -> ) } [ Expression   18 ]
N w ′  ( x -> ) = N w  ( x -> ) · exp ( - E w  ( x -> ) 2 σ Eth 2 ) [ Expression   19 ]
n ij ′  ( x _ ) = n ij  ( x -> ) · exp ( - e ij  ( x -> ) 2 σ ethij 2 ) [ Expression   22 ]
N w  ( x _ ) = ∑ i = LL , LH , HL , HH j = 1 , 2 ,  …  , 5  Wavelet - 
1  { k nij · n ij ′  ( x -> ) } [ Expression   23 ]
E w  ( x -> ) = ∑ i = LL , LH , HL , HH j = 1 , 2 ,  …  , 5  Wavelet - 1  { k eij · e ij  ( x -> ) } [ Expression   24 ]
λ  ( x -> ) = λ 0 · exp ( - E w  ( x -> ) 2 σ Eth 2 ) exp  ( - α 2 σ Eth 2 )   λ   0 = const .  α = const . [ Expression   25 ]
S ′  ( x -> ) = ∫  x -> ′ - x _  ≤ 2  r th  S  ( x -> ′ )  exp  ( -  S  ( x _ ′ ) - S  ( x -> )  2 σ th 2 ·  x -> ′ - x _  2 r th 2 )   x _ ′ ∫  x -> ′ - x _  ≤ 2  r th  exp  ( -  S  ( x -> ′ ) - S  ( x -> )  2 σ th 2 ·  x -> ′ - x _  2 r th 2 )   x -> ′ [ Expression   28 ]
∇ 2  =  1 - ( Gaussian ) 25 × 25 =  1 - ( Gaussian ) 9 × 9 ⊗ ( Gaussian ) 9 × 9 ⊗ ( Gaussian ) 9 × 9 [ Expression   31 ]
λ  ( x _ ) = λ 0 · exp  ( - E  ( x _ ) 2 σ Eth 2 ) exp  ( - α 2 σ Eth 2 )   λ   0 = const .  α = const . [ Expression   32 ]
 V ′  ( x -> ) = V  ( x -> ) - ∇ 2  V  ( x _ ) · exp ( -  ∇ 2  V  ( x _ )  2 σ th 2 )    V ″  ( x -> ) = V ′  ( x -> ) + ∇ 2  V ′  ( x -> ) · exp ( -  ∇ 2  V ′  ( x -> )  2 σ th 2 )    V ′′′  ( x _ ) = V ″  ( x -> ) - ∇ 2  V ″  ( x -> ) · exp ( -  ∇ 2  V ″  ( x _ )  2 σ th 2 )   V ( 4 )  ( x _ ) = V ′′′  ( x _ ) + ∇ 2  V ′′′  ( x -> ) · exp ( -  ∇ 2  V ′′′  ( x -> )  2 σ th 2 ) [ Expression   35 ]
V ′  ( x _ ) = V  ( x _ ) - ∇ 2  V  ( x -> ) · exp ( -  ∇ 2  V  ( x -> )  2 σ th 2 )   V ″  ( x _ ) = V ′  ( x -> ) - ∇ 2  V ′  ( x -> ) · exp ( -  ∇ 2  V ′  ( x _ )  2 σ th 2 ) [ Expression   36 ]
∇ 2  =  1 - ( Gaussian ) 9 × 9 =  1 - ′  ( 1 8 28 56 70 56 28 8 1 ) 256 ⊗ ( 1 8 28 56 70 56 28 8 1 ) 256 [ Expression   37 ]
a quantity of an edge component present in the extracted noise component is estimated based upon a level of the synthesized edge component
10. An image processing method according to claim 7, wherein:
US13/731,736 2007-02-02 2012-12-31 Image processing for removing a noise component contained in an image Active US8675982B2 (en)
US12/458,855 US8369640B2 (en) 2007-02-02 2009-07-24 Image processing for removing a noise component contained in an image
US13/731,736 US8675982B2 (en) 2007-02-02 2012-12-31 Image processing for removing a noise component contained in an image
US12/458,855 Division US8369640B2 (en) 2007-02-02 2009-07-24 Image processing for removing a noise component contained in an image
US8675982B2 US8675982B2 (en) 2014-03-18
US12/458,855 Active 2030-02-16 US8369640B2 (en) 2007-02-02 2009-07-24 Image processing for removing a noise component contained in an image
US13/731,736 Active US8675982B2 (en) 2007-02-02 2012-12-31 Image processing for removing a noise component contained in an image
TW201318418A (en) * 2011-10-26 2013-05-01 Novatek Microelectronics Corp Method of noise reduction in image and device thereof
2008-02-01 WO PCT/JP2008/051652 patent/WO2008093836A1/en active Application Filing
2008-02-01 JP JP2008556197A patent/JP4766118B2/en active Active
2009-07-24 US US12/458,855 patent/US8369640B2/en active Active
2012-12-31 US US13/731,736 patent/US8675982B2/en active Active
JPWO2008093836A1 (en) 2010-05-20
US8675982B2 (en) 2014-03-18
WO2008093836A1 (en) 2008-08-07
US20090290067A1 (en) 2009-11-26
JP4766118B2 (en) 2011-09-07
US8369640B2 (en) 2013-02-05
JP4711962B2 (en) 2011-06-29 Method and apparatus for modeling a pattern of film grain in the frequency domain
KR100754181B1 (en) 2007-09-03 Method and apparatus for reducing mosquito noise in fecoded video sequence
JP2005117549A (en) 2005-04-28 Image processing method, image processor, and image processing program
JPWO2004112401A1 (en) 2006-07-20 Image processing method, image processing program, the image processing apparatus
JP2001229377A (en) 2001-08-24 Method for adjusting contrast of digital image by adaptive recursive filter
JPWO2002093935A1 (en) 2004-09-02 Image processing apparatus