1. Scatter-plot Based Blind Estimation of Mixed Noise Parameters
RMSW’2012 1
Scatter-plot Based Blind Estimation
of Mixed Noise Parameters
for Remote Sensing Image Processing
V. Abramova1, S. Abramov1, V. Lukin1,
B. Vozel2, K. Chehdi2
1 Department of Transmitters, Receivers and Signal Processing,
National Aerospace University, 17 Chkalova Street, 61070, Kharkov, Ukraine s:
2University of Rennes 1,
IETR UMR CNRS 6164, 6 Kerampont Street, 22305, Lannion, France
National Aerospace University ”KhAI”
Vladimir Lukin
24/09/2012

2. Need in blind noise characteristics evaluation
Introduction 2
Applications: program complexes for processing images formed by different imaging
systems (IS).
Goal: design of blind noise characteristics evaluation procedure taking into account signal-dependent character of the noise.
Images
Noise
Image processing
Signal-independent
Signal-dependent
Impulse
Filtering
Reason:
circuit and atmospheric noise
Reason: insufficient observation interval, counting principle of IS sensors operation
Reason:
Transmitting and encoding/decodingerrors
Edge detection
Recognition
Mixed
Need in blind noise characteristics evaluation
National Aerospace University ”KhAI”
Vladimir Lukin
24/09/2012

3. Main stages of the basic blind mixed noise variance evaluation method3
1. Image is segmented and homogeneous regions are detected.
2. Image is divided into overlapping square-shape blocks of size n x n.
3. For each block belonging to homogeneous region a pair of local parameters is calculated:
- mean:
; variance:
4. Using the obtained pairs of local parameters, a scatter-plot is formed.
5. For each scatter-plot cluster its mode is found separately by each coordinate
6. Through the found cluster modes, linear approximation is fitted by DWLMS algorithm and both additive noise variance and quasi-Poissonian noise parameter estimates are obtained.
National Aerospace University ”KhAI”
Vladimir Lukin
24/09/2012

4. Image pre-segmentation and homogenous regions detection4
Pre-segmentation method is based on optimized histogram transformation by gravitational clustering with followed image pixels variation classification
Segmented image
Classification map (block size 8х8):
grey – homogenous regions;
black – regions with edges; white – texture regions
National Aerospace University ”KhAI”
Vladimir Lukin
24/09/2012

5. Basic method accuracy analysis5
Scatter-plot of local estimates for image #6
from TID2008 database and approximation lines: true (black) and obtained in a blind manner (red)
Scatter-plot of local estimates for image #13 from TID2008 database and approximation lines: true (black) and obtained in a blind manner (red)
Conclusion: lines obtained in a blind manner are fitted quite accurately through the cluster centers, but many cluster centers are essentially biased from the true line. As a result, noise parameter estimates also occur essentially biased wrt their true values. That’s why, another procedure for obtaining cluster centers is needed.
National Aerospace University ”KhAI”
Vladimir Lukin
24/09/2012

6. Proposed cluster centers’ obtaining procedure6
Detection of blocks belonging to image homogeneous regions within each cluster
Calculation of DCT coefficients D(k,l) and grouping them according to their indices k and l
Estimation of PCK(k,l) and MAD(k,l) for k+l>8
Q3, Q1 – 3rd and 1st quartiles;
P90, P10 – 90% and 10% percentiles
, Hm is number of blocks in m-th cluster
Determination of index pairs for which 0.25<PCK(k,l)<0.276 (where distribution of D(k,l) is close to Gaussian), number of such pairs Nintm is determined as well
yes no
Nintm = 0?
is obtained as mean of the corresponding cluster elements after pre-segmentation
National Aerospace University “KhAI”
Vladimir Lukin
24/09/2012

7. Test Images Database TID2008 (Tampere Image Database)7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
National Aerospace University ”KhAI”
Vladimir Lukin
24/09/2012

8. Test images and noise model8
TID2008 contains 25 noise-free (high quality) color images where first 24 are the fragments of original Kodak database and the 25-th image is the artificially created image with different textures. All color images are RGB 24-bit ones of size 512x384 pixels.
Images # 1, 5, 8, 13, 14 and 18 are the most highly textural images in database.
TID2008 image #25
Noise model:
where true (noise-free) image;
- quasi-Poissonian noise component;
- gain;
- additive noise component with zero mean and variance
National Aerospace University ”KhAI”
Vladimir Lukin
24/09/2012

9. Comparative analysis of the basic and proposed methods performance9
Scatter-plot of local estimates for image #6
from TID2008 database and approximation lines: true (black) and obtained in a blind manner using basic (red) and proposed (green) methods
Scatter-plot of local estimates for image #13
from TID2008 database and approximation lines: true (black) and obtained in a blind manner using basic (red) and proposed (green) methods
Conclusion: cluster centers obtained using the proposed technique based on DCT coefficients statistical analysis are much closer to the true line than those obtained using basic method (mode estimates by each coordinate). The noise parameters’ estimates are also less biased wrt their true values.
National Aerospace University ”KhAI”
Vladimir Lukin
24/09/2012

10. Results analysis: signal-independent noise component10
Red component
Green component
Image index in TID2008 database
Image index in TID2008 database ,
Note: basic method
- proposed method
- true line ( )
Blue component
Conclusion: the estimates of additive noise variance for the proposed method are, in general, more accurate than for the basic method. Although for highly textured images these estimates are also essentially biased wrt the true value of noise variance.
Image index in TID2008 database
National Aerospace University ”KhAI”
Vladimir Lukin
24/09/2012

11. Results analysis: signal-dependent noise component11
Red component
Green component
Image index in TID2008 database
Image index in TID2008 database ,
Note: basic method
- proposed method
- true line ( )
Blue component
Conclusion: the estimates also have several “outliers” and they mostly happen for the most textural test images. Meanwhile, the estimates for the proposed method are, on the average, more accurate than for the basic method.
Image index in TID2008 database
National Aerospace University ”KhAI”
Vladimir Lukin
24/09/2012

12. Conclusions 12
General accuracy of blind method for mixed signal-independent and signal-dependent noise parameters evaluation greatly depends on the accuracy of the stage when scatter-plot clusters centers are obtained.
A novel procedure for obtaining scatter-plot clusters centers has been proposed. It is based on DCT coefficients statistical characteristics analysis within each cluster.
The proposed method allows providing more accurate estimation of cluster centers. In turn, this leads to higher accuracy of estimating the mixed noise parameters especially for highly textural images.
One drawback of blind estimation methods operating in spectral domain is that they produce considerably biased local estimates if noise is spatially correlated. Thus, our future research will be focused on finding ways to get around this drawback.
National Aerospace University ”KhAI”
Vladimir Lukin
24/09/2012