Signal separation method, signal processing apparatus, image processing apparatus, medical image processing apparatus and storage medium for restoring multidimensional signals from observed data in which multiple signals are mixed

• US 20020085741A1
• Filed: 10/29/2001
• Published: 07/04/2002
• Est. Priority Date: 11/01/2000
• Status: Active Grant

• Current Assignee: Uniloc 2017 LLC (SoftBank Group Corp.)
• Original Assignee: International Business Machines Corporation
• Inventors: Shimizu, Junya
• Granted Patent: US 6,944,579 B2
• Time in Patent Office: Days
• US Class Current: 382/118
• CPC Class Codes: G06F 18/21342 using statistical independence, i.e. minimising mutual information or maximising non-gaussianity
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening
• CPC Class Codes: G06V 10/7715 Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods

Abstract

Signals are separated by introducing a function having a monotonously increasing characteristic like an exponential type function as a cost function, and applying an adaptive algorithm that minimizes that cost function in terms of a signal separation matrix. That is, there is provided a signal processing apparatus that separates and outputs an original signal from the observed signal x(t), in which multiple multidimensional signals are mixed, wherein the nonlinear function 21 is operated on an input observed signal x(t) and an estimated separation matrix W(t−1) estimated at a previous cycle. Then, an error signal e(t) is calculated 22 based on y(t) formed by this nonlinear function 21, the estimated separation matrix W(t−1) estimated at the previous cycle, and the observed signal x(t) at that time. Then, based on the calculated error signal e(t), the update of the separation matrix W(t) at that time is performed 23 such that consideration weight is increased when estimation errors are large using the cost function having a monotonously increasing characteristic.

Claims

1. A signal separation method for restoring original multidimensional signals from observed data where multiple multidimensional signals are mixed, the method comprising the steps of:

selecting a specific separation matrix by optimizing at least either one of H-infinity norm type of cost function or MinMax strategy type of cost function for said observed data; and

multiplying the observed data by the selected separation matrix to restore the original multidimensional signals.

2. A signal separation method for estimating and restoring original multidimensional signals from observed data where multiple signals including the original multidimensional signals are mixed, the method comprising the steps of:

introducing a cost function for the observed data, the cost function being based on a function that has a monotonously increasing characteristic;

estimating a separation matrix using an adaptive filter that optimizes the introduced cost function; and

multiplying the observed data by the estimated separation matrix to estimate and restore the original multidimensional signals.

3. The method according to claim 2, wherein the step of estimating a separation matrix comprises using the adaptive filter that minimizes the cost function in terms of the separation matrix, wherein the introduced cost function is an exponential type function.

4. A method for separating and extracting original signals from observed signals where multiple multidimensional signals including the original signals are mixed, the method comprising the steps of:

reading the observed signals;

transforming a data structure in terms of the read observed signals;

subtracting an average of the observed signals of which data structure has been transformed to perform zero averaging;

performing whitening on the observed signals that have undergone zero averaging;

performing separation processing on the observed signals that have undergone whitening based on a cost function having a monotonously increasing characteristic; and

performing inverse whitening as post-processing on the observed signals that have undergone the separation processing.

5. A signal processing apparatus, comprising:

means for inputting observed data where multiple signals including original multidimensional signals are mixed;

means for selecting a specific separation matrix by optimizing at least either one of H-infinity norm type of cost function or MinMax strategy type of cost function for the input observed data; and

means for multiplying the observed data by the selected separation matrix to estimate and restore the original multidimensional signals.

6. A signal processing apparatus, comprising:

means for inputting observed data where multiple multidimensional signals that are originally independent are mixed mutually;

means for estimating a separation matrix by using an adaptive filter for the input observed data, the filter optimizing a cost function based on a function that has a monotonously increasing characteristic; and

means for multiplying the observed data by the estimated separation matrix to estimate and restore the specific multidimensional signals.

7. The signal processing apparatus according to claim 6, wherein the observed data input by the input means comprises given image data that consist of mixtures of moving picture data that are considered as three dimensional data.

8. An image processing apparatus, comprising:

means for inputting mixed image data where multiple image signals are mixed and observed;

means for estimating a separation matrix by using an adaptive filter for the input mixed image data, the filter optimizing a cost function based on a function having a monotonously increasing characteristic; and

means for multiplying the mixed image data by the estimated separation matrix to separate and extract the image signals from the mixed image data.

9. The image processing apparatus according to claim 8, wherein the adaptive filter used minimizes the cost function in terms of the separation matrix, and the function having a monotonously increasing characteristic is an exponential type function.

10. The image processing apparatus according to claim 8, wherein the input mixed image data comprises text overwritten on a background image and are observed as multiple frames.

11. The image processing apparatus according to claim 8, wherein the image signals that are separated and extracted are image signals that are used in face recognition and distinction in biometrics or image signals that are obtained by the observation and measurement by satellites.

12. A medical image processing apparatus, comprising:

means for inputting medical image data where a signal change of which interference process is unknown is contained in observed bio-image signals;

means for estimating a separation matrix by using an adaptive filter for the input medical image data, the filter optimizing a cost function based on a function having a monotonously increasing characteristic; and

means for multiplying the medical image data by the estimated separation matrix to separate and extract the observed bio-image signals from the medical image data.

13. A storage medium tangibly embodying a program readable and executable by a computer, the program comprising:

processing for introducing a cost function for observed data where multiple multidimensional signals are mixed, the cost function being based on a function that has a monotonously increasing characteristic;

processing for estimating a separation matrix using an adaptive filter that optimizes the introduced cost function; and

processing for multiplying the observed data by the estimated separation matrix to estimate and restore the specific multidimensional signals.

14. A storage medium tangibly embodying a program readable and executable by a computer, the program comprising:

processing for selecting a specific separation matrix by optimizing at least either one of H-infinity norm type of cost function or MinMax strategy type of cost function for observed data where multiple multidimensional signals are mixed; and

processing for multiplying the observed data by the selected separation matrix to estimate and restore the specific multidimensional signal.