IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, AND PROGRAM

• US 20130135496A1
• Filed: 11/19/2012
• Published: 05/30/2013
• Est. Priority Date: 11/29/2011
• Status: Abandoned Application

• Current Assignee: Sony Corporation (Sony Group Corp.)
• Original Assignee: Sony Corporation (Sony Group Corp.)
• Inventors: Nomura, Yoshikuni
• Application Number:
  US13/680,443
• Publication Number:
  US 20130135496A1
• Time in Patent Office: Days
• US Class Current: 348/231.6
• CPC Class Codes: G06F 18/21 Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
• CPC Class Codes: G06F 18/24155 Bayesian classification
• CPC Class Codes: G06V 10/143 Sensing or illuminating at different wavelengths
• CPC Class Codes: G06V 10/30 Noise filtering
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects

Abstract

An image processing device includes: an image probability model generation unit calculating a feature amount in units of local regions as division regions of a captured image of an imaging apparatus and generating an image probability model configured by the calculated feature amount, the image probability model indicating the generation probability of each noiseless pixel value; a memory storing a noise probability model generated from imaging element-dependent noise characteristic information, the noise probability model indicating the conditional probability of a given noised pixel value being generated in a case where a given noiseless pixel value is generated; and a Bayesian estimation unit generating a noise reduced image in which the noise of the captured image is reduced through a Bayesian estimation process in which the image probability model and the noise probability model are applied.

Claims

1. An image processing device comprising:

an image probability model generation unit calculating a feature amount in units of local regions as division regions of a captured image of an imaging apparatus and generating an image probability model configured by the calculated feature amount, the image probability model indicating a generation probability of each noiseless pixel value;

a memory storing a noise probability model generated from imaging element-dependent noise characteristic information, the noise probability model indicating a conditional probability of a given noised pixel value being generated in a case where a given noiseless pixel value is generated; and

a Bayesian estimation unit generating a noise reduced image in which the noise of the captured image is reduced through a Bayesian estimation process in which the image probability model and the noise probability model are applied.

2. The image processing device according to claim 1,

wherein the image probability model generation unit includes:

a local pixel selection unit selecting, from a local region including a noise reduction process target pixel, a pixel in which a pixel value difference with the noise reduction process target pixel is equal to or less than a threshold value as a reference pixel; and

a local mean variance calculation unit calculating a mean value and a variance value of the reference pixel selected by the local pixel selection unit,

wherein the image probability model is an approximate image probability model formed of a calculation value of the local mean variance calculation unit.

3. The image processing device according to claim 1,

wherein the noise probability model stored in the memory is an approximate noise probability model generated by applying a Gaussian mixture model approximation representing an arbitrary distribution by adding a plurality of Gaussian distributions.

4. The image processing device according to claim 1,

wherein the noise probability model stored in the memory is an approximate noise probability model generated by applying a Gaussian mixture model approximation representing an arbitrary distribution by adding a plurality of Gaussian distributions, and

parameters of the Gaussian mixture model approximation are parameters calculated by applying an EM (Expectation-Maximization) algorithm.

5. The image processing device according to claim 1,

wherein the noise probability model stored in the memory is a noise probability model generated by applying simulation process data virtually generating a pixel value in which noise signals according to a plurality of noise generation causes occurring on a captured image of an imaging element overlap.

6. The image processing device according to claim 1,

wherein the image probability model generation unit generates an approximate image probability model formed of a single normal distribution,

the noise probability model stored in the memory is an approximate noise probability model generated by applying a Gaussian mixture model approximation representing an arbitrary distribution by adding a plurality of Gaussian distributions, and

the Bayesian estimation unit generates a noise reduced image in which the noise of the captured image is reduced through a Bayesian estimation process applying the approximate image probability model and the approximate noise probability model.

7. The image processing device according to claim 1,

wherein the image processing device further includes:

a noise probability model generation unit generating the noise probability model,

wherein the noise probability model generation unit includes

a noise simulation processing unit virtually generating a pixel value in which noise signals according to a plurality of noise generation causes occurring on a captured image of an imaging element overlap, and

a Gaussian model approximation unit generating an approximate noise probability model through a Gaussian mixture model (GMM) approximation process on data generated by the noise simulation processing unit.

8. An imaging apparatus comprising:

an imaging unit including an imaging element;

an image probability model generation unit calculating a feature amount in units of local regions as division regions of a captured image input from the imaging unit and generating an image probability model configured by the calculated feature amount, the image probability model indicating a generation probability of each noiseless pixel value;

a memory storing a noise probability model generated from imaging element-dependent noise characteristic information, the noise probability model indicating a conditional probability of a given noised pixel value being generated in a case where a given noiseless pixel value is generated; and

a Bayesian estimation unit generating a noise reduced image in which the noise of the captured image is reduced through a Bayesian estimation process in which the image probability model and the noise probability model are applied.

9. An image processing method executing on an image processing device, comprising:

an image probability model generating process including calculating a feature amount in units of local regions as division regions of a captured image of an imaging apparatus and generating an image probability model configured by the calculated feature amount, the image probability model indicating a generation probability of each noiseless pixel value; and

a Bayesian estimation process generating a noise reduced image in which the noise of the captured image is reduced through Bayesian estimation by applying a noise probability model generated from imaging element-dependent noise characteristic information, the noise probability model indicating a conditional probability of a given noised pixel value being generated in a case where a given noiseless pixel value is generated, and the image probability model.

10. A program causing an image process to be executed on an image processing device, comprising:

an image probability model generating process including calculating a feature amount in units of local regions as division regions of a captured image of an imaging apparatus and generating an image probability model configured by the calculated feature amount, the image probability model indicating a generation probability of each noiseless pixel value; and

a Bayesian estimation process generating a noise reduced image in which the noise of the captured image is reduced through Bayesian estimation by applying a noise probability model generated from imaging element-dependent noise characteristic information, the noise probability model indicating a conditional probability of a given noised pixel value being generated in a case where a given noiseless pixel value is generated, and the image probability model.