Digital image processing method in particular for satellite images

• US 20040234162A1
• Filed: 07/12/2004
• Published: 11/25/2004
• Est. Priority Date: 07/30/2001
• Status: Abandoned Application

• Current Assignee: Centre National De La Recherche Scientifique, Inria Institut National de Recherche EN Informatique et EN Automatique
• Original Assignee: Centre National De La Recherche Scientifique, Inria Institut National de Recherche EN Informatique et EN Automatique
• Inventors: Zerubia, Josiane, Blanc-Feraud, Laure, Jalobeanu, Andre
• Application Number:
  US10/485,090
• Publication Number:
  US 20040234162A1
• Time in Patent Office: Days
• US Class Current: 382/280
• CPC Class Codes: G06T 2207/10032 Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/20052 Discrete cosine transform [DCT]
• CPC Class Codes: G06T 2207/20056 Discrete and fast Fourier transform, [DFT, FFT]
• CPC Class Codes: G06T 5/00 Image enhancement or restoration
• CPC Class Codes: G06T 5/10 using non-spatial domain filtering

Abstract

The invention concerns processing of digital images, captured by detection of electromagnetic waves, such as satellite pictures. The inventive processing consists in applying a parameterable fractal modelling (M) to Fourier transforms of the pixels of the image and comparing (22) the thus modelled transforms (a_ij^{q, wo}) to the initial transforms (a_ij) to bring the parameters (q, w₀) closer to the fractal model, and if required, the parameters (α,μ,σ) of a transfer function of the instrument which has captured the image.

Claims

1. Method of processing digital images acquired by the detection of electromagnetic waves, characterised in that it comprises the following steps:

a) recovering (10) image data (e_ij) relating to constituent elements of an initial image,

b) applying (12) at least one spectral transformation (FFT, DCT) to at least some of the image elements,

c) in the case of at least some of these elements, applying (16) to the transforms (a_ij) of the elements an overall statistical modelling (M) which can be set to parameters, and

d) comparing (22) the modelled transforms (a_ij^qw0) with the initial transforms (a_ij) in order to obtain a close approximation of at least one parameter (q, w₀, α

, μ

, σ

) which comes into play in the statistical model applied.

2. Method according to claim 1, characterised in that the statistical model is of the fractal type and comprises the assignment of at least one parameter (q) which is suitable for defining a statistical variation (w₀.r⁻

9) of said transforms.

3. Method according to either of claims 1 and 2, characterised in that said spectral transformation in step b) is of the Fourier transformation type, and in that the statistical model covers Fourier coefficients (a_ij) resulting from the transformation.

4. Method according to either of claims 1 and 2, characterised in that said transformation in step b) is of the discrete cosine transformation (DCT) type, and in that the statistical model covers coefficients (a_ij) resulting from the transformation.

5. Method according to either of claims 3 and 4 taken in combination with claim 2, characterised in that the parameter (q, w₀) of the fractal model is suitable for defining a statistical variation (w₀.r⁻

9) of said coefficients in the frequency domain (x).

6. Method according to claim 5, characterised in that said statistical variation substantially follows a Gaussian curve, and in that the model assigns two parameters (q, w₀), one (q) of the parameters of the fractal model being representative of the attenuation of the Gaussian curve with distance from its axis and the other parameter (w₀) being a multiplying coefficient.

7. Method according to one of the foregoing claims, characterised in that step d) comprises finding an extremum in a mathematical expression (-log(P)) representing said comparison.

8. Method according to claim 7, characterised in that the comparison in step c) comprises finding a maximum probability (P).

9. Method according to claim 8 taken in combination with one of claims 5 to 7, characterised in that the finding of the maximum probability brings into play a probability (P) density over the entire frequency domain, which involves all the elements of the image (N_xN_y)

10. Method according to claim 9, characterised in that it makes provision for linear optimisation (56) of the probability density.

11. Method according to claim 10, characterised in that the linear optimisation employs a gradient descent calculation.

12. Method according to one of the foregoing claims in which the statistical model brings into play at least one first parameter (α

, μ

, σ

) and at least one second parameter (q, w₀), characterised in that the comparison step c) comprises the following operations;

c1) assigning (26) an approximate value (α

, μ

, σ

) to the first parameter,

c2) assigning (18) an approximate value (Q, W₀) to the second parameter,

c3) comparing (22;

74) the modelled transforms with the initial transforms, and

c4) successively adjusting (18) the value of the second parameter (q, w₀) by repeating operations c2) and c3).

13. Method according to claim 12, characterised in that step c) also comprises the following operations:

c5) laying down (72) the value of the second parameter (q, w₀) as adjusted in operation c4), and

c6) successively adjusting (76) the value of the first parameter (α

, μ

, σ

) by repeating operations c1) and c3).

14. Method according to claim 13 taken in combination with claim 8, characterised in that it comprises a repetition of operations c1), c2), c3), c4), c5) and c6) until values which substantially correspond to the maximum probability are obtained for the first and second parameters.

15. Method according to one of the foregoing claims in which the statistical model brings into play at least one first parameter (α

) and one second parameter (q), characterised in that the comparison step c) comprises the following operations;

c1) determining (84) a dependence between the first and second parameters (q, α

), preferably a dependence of the linear regression type,

c2) laying down (88) the second parameter (q), and c3) deriving therefrom (86) an estimate of the first parameter (α

).

16. Method according to one of the foregoing claims, characterised in that the statistical model also brings into play at least one instrument parameter (α

, μ

, σ

) which is subject to variations, and in that, in step c), a close approximation is obtained (30;

58) of this instrument parameter, which enables the initial image to be processed (60, 62, 64, 66, 68) to increase the quality thereof.

17. Method according to claim 16, characterised in that the instrument parameters is suitable for quantitatively representing an image degradation due to one of the following events:

diffusion of the electromagnetic waves during detection (FTM_det), defocusing and/or an aberration in the forming of the image (FTM_opt), electronic noise at reception (N).

18. Method according to claim 17, characterised in that it comprises a step prior to step b), for modelling (40, 42, 46) an instrument modulation function associated with at least one of said events, this function bringing into play said instrument parameter.

19. Method according to claim 18 taken in combination with claim 2 and one of claims 12 to 15, characterised in that said first parameter (α

, μ

, σ

) is intrinsic to the model of the instrument modulation function (FTM, N) whereas the second parameter (q, w₀) is intrinsic to the fractal model.

20. Method according to either of claims 18 and 19 taken in combination with claim 6, characterised in that the modulation function comprises at least one envelope of Gaussian appearance.

21. Application of the method according to one of the foregoing claims to the processing of satellite or aerial images obtained by optical or infrared detection.

22. Device for performing the method according to one of claims 1 to 20, characterised in that it comprises a statistical modelling module (MOD), comprising an input for recovering spectral transforms of constituent elements (a_ij) of an initial image and arranged:

to apply overall statistical modelling, which can be set to parameters, to at least some of the element transforms, and

to compare the modelled transforms with the initial transforms, with a view to obtaining a close approximation of at least one parameter q, w₀, α

, μ

, σ

) which comes into play in the statistical model applied.

23. Device according to claim 22, characterised in that it comprises memory means (MEM) containing program data relating to the modelling module and calculating means (μ

P) arranged to co-operate with the memory means to put the modelling module (MOD) into practical operation.

24. Device according to claim 22, characterised in that the modelling module comprises memory means and calculating means which are combined in one and the same component (FPGA, VLSI).

25. Device according to one of claims 22 to 24, characterised in that it is intended to be carried on board an aerial vehicle (SAT).

26. Device according to one of claims 22 to 25, characterised in that it comprises an output (L2) suitable for supplying said parameter of the statistical model.

27. Computer software product intended to be stored in a device according to one of claims 22 to 26 to put at least the modelling module (MOD) into practical operation.