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US 10,715,192 B2Granted
Signal processing apparatus, signal processing method, and storage medium
AssigneeCanon Kabushiki Kaisha
InventorsTetsuya Suwa, Tomokazu Ishikawa, Wakako Tanaka, Shinjiro Hori
Life of this patent
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Abstract
A signal processing apparatus includes a unit configured to generate noise cut data by deducting a predetermined noise value from values of respective signals constituting input data and a stochastic resonance processing unit configured to subject the noise cut data to a predetermined stochastic resonance processing. The predetermined stochastic resonance processing is processing to output, in a method of synthesizing a result of parallelly performing steps of adding new noise to the noise cut data to subject the resultant data to a binary processing, a value obtained in a case where the parallel number is infinite.
Claims15
A signal processing apparatus comprising:
- circuitry or at least one processor, configured to act as a plurality of units comprising:
- (1) an acquisition unit configured to acquire input data;
- (2) a unit configured to generate noise cut data by deducting uniformly a predetermined fixed value from values of respective signals of the input data;
- (3) a stochastic resonance processing unit configured to subject the noise cut data to a predetermined stochastic resonance processing to thereby generate stochastic resonance data;
- and
- (4) an output unit configured to output the result of the predetermined stochastic resonance processing,
- wherein the predetermined stochastic resonance processing is processing to output, in a method of adding different noises to the same noise cut data and performing binary processing in a plurality of branch paths respectively, a convergence value obtained in a case where a parallel number is increased,
- wherein the predetermined stochastic resonance processing is performed by using the following formula to calculate processed data J(x) from the input data I(x):
- J
-
- (
- x
- )
- =
- {
- 1
- T
- <
- I
-
- (
- x
- )
- 0
- I
-
- (
- x
- )
- <
- T
- -
- K
- 1
- -
- (
- T
- -
- 1
-
- (
- x
- )
- )
- /
- K
- T
- -
- K
- ≦
- I
-
- (
- x
- )
- ≦
- T
- where T is a threshold value to quantize the input data and K is a noise strength,
- or the predetermined stochastic resonance processing is performed by using the following formula to calculate processed data J(x) from the input data I(x):
- J
-
- (
- x
- )
- =
- {
- 1
- T
- <
- I
-
- (
- x
- )
- 0
- I
-
- (
- x
- )
- <
- T
- -
- K
- 1
- -
- 1
- 1
- +
- exp
-
- {
- -
- α
-
- (
- (
- T
- -
- I
-
- (
- x
- )
- )
- K
- -
- 0.5
- )
- }
- T
- -
- K
- ≦
- I
-
- (
- x
- )
- ≦
- T
- where T is a threshold value to quantize the input data and K is a noise strength.
119 more elements
A signal processing apparatus comprising:
- circuitry or at least one processor, configured to act as a plurality of units comprising:
- (1) an acquisition unit configured to acquire input data;
- (2) a unit configured to generate noise cut data by deducting uniformly a predetermined fixed value from values of respective signals of the input data;
- (3) a stochastic resonance processing unit configured to subject the input data to a predetermined stochastic resonance processing to subsequently multiply the noise cut data to thereby generate stochastic resonance data;
- and
- (4) an output unit configured to output the result of the predetermined stochastic resonance processing,
- wherein the predetermined stochastic resonance processing is processing to output, in a method of adding different noises to the same input data and performing binary processing in a plurality of branch paths respectively, a convergence value obtained in a case where a parallel number is increased,
- wherein the predetermined stochastic resonance processing is performed by using the following formula to calculate processed data J(x) from the input data I(x):
- J
-
- (
- x
- )
- =
- {
- 1
- T
- <
- I
-
- (
- x
- )
- 0
- I
-
- (
- x
- )
- <
- T
- -
- K
- 1
- -
- (
- T
- -
- I
-
- (
- x
- )
- )
- /
- K
- T
- -
- K
- ≦
- I
-
- (
- x
- )
- ≦
- T
- where T is a threshold value to quantize the input data and K is a noise strength,
- or the predetermined stochastic resonance processing is performed by using the following formula to calculate processed data J(x) from the input data I(x),
- J
-
- (
- x
- )
- =
- {
- 1
- T
- <
- I
-
- (
- x
- )
- 0
- I
-
- (
- x
- )
- <
- T
- -
- K
- 1
- -
- 1
- 1
- +
- exp
-
- {
- -
- α
-
- (
- (
- T
- -
- I
-
- (
- x
- )
- )
- K
- -
- 0.5
- )
- }
- T
- -
- K
- ≦
- I
-
- (
- x
- )
- ≦
- T
- where T is a threshold value to quantize the input data and K is a noise strength.
119 more elements
A signal processing method comprising:
- an acquisition step of acquiring input data;
- a step of generating noise cut data by deducting uniformly a predetermined fixed value from values of respective signals of the input data;
- a stochastic resonance processing step of subjecting the noise cut data to a predetermined stochastic resonance processing to thereby generate stochastic resonance data;
- and
- an output step of outputting the result of the predetermined stochastic resonance processing,
- wherein the predetermined stochastic resonance processing is a processing to output, in a method of adding different noises to the same noise cut data and performing binary processing in a plurality of branch paths respectively, a convergence value obtained in a case where a parallel number is increased,
- wherein the predetermined stochastic resonance processing is performed by using the following formula to calculate processed data J(x) from the input data I(x):
- J
-
- (
- x
- )
- =
- {
- 1
- T
- <
- I
-
- (
- x
- )
- 0
- I
-
- (
- x
- )
- <
- T
- -
- K
- 1
- -
- (
- T
- -
- I
-
- (
- x
- )
- )
- /
- K
- T
- -
- K
- ≦
- I
-
- (
- x
- )
- ≦
- T
- where T is a threshold value to quantize the input data and K is a noise strength,
- or the predetermined stochastic resonance processing is performed by using the following formula to calculate processed data J(x) from the input data I(x):
- J
-
- (
- x
- )
- =
- {
- 1
- T
- <
- I
-
- (
- x
- )
- 0
- I
-
- (
- x
- )
- <
- T
- -
- K
- 1
- -
- 1
- 1
- +
- exp
-
- {
- -
- α
-
- (
- (
- T
- -
- I
-
- (
- x
- )
- )
- K
- -
- 0.5
- )
- }
- T
- -
- K
- ≦
- I
-
- (
- x
- )
- ≦
- T
- where T is a threshold value to quantize the input data and K is a noise strength.
118 more elements
A non-transitory computer-readable storage medium which stores a program for causing a computer to execute a signal processing method, the signal processing method comprising:
- an acquisition step of acquiring input data;
- a step of generating noise cut data by deducting uniformly a predetermined fixed value from values of respective signals of the input data;
- a stochastic resonance processing step of subjecting the noise cut data to a predetermined stochastic resonance processing to thereby generate stochastic resonance data;
- and
- an output step of outputting the result of the predetermined stochastic resonance processing,
- wherein the predetermined stochastic resonance processing is a processing to output, in a method of adding different noises to the same noise cut data and performing binary processing in a plurality of branch paths respectively, a convergence value obtained in a case where a parallel number is increased,
- wherein the predetermined stochastic resonance processing is performed by using the following formula to calculate processed data J(x) from the input data I(x):
- J
-
- (
- x
- )
- =
- {
- 1
- T
- <
- I
-
- (
- x
- )
- 0
- I
-
- (
- x
- )
- <
- T
- -
- K
- 1
- -
- (
- T
- -
- I
-
- (
- x
- )
- )
- /
- K
- T
- -
- K
- ≦
- I
-
- (
- x
- )
- ≦
- T
- where T is a threshold value to quantize the input data and K is a noise strength,
- or the predetermined stochastic resonance processing is performed by using the following formula to calculate processed data J(x) from the input data I(x):
- J
-
- (
- x
- )
- =
- {
- 1
- T
- <
- I
-
- (
- x
- )
- 0
- I
-
- (
- x
- )
- <
- T
- -
- K
- 1
- -
- 1
- 1
- +
- exp
-
- {
- -
- α
-
- (
- (
- T
- -
- I
-
- (
- x
- )
- )
- K
- -
- 0.5
- )
- }
- T
- -
- K
- ≦
- I
-
- (
- x
- )
- ≦
- T
- where T is a threshold value to quantize the input data and K is a noise strength.
118 more elements
Dependencies are inferred from the claim text.