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Grey level and noise evaluation of a Foveon X3 image sensor: a statistical and experimental approach.

Riutort-Mayol G, Marqués-Mateu A, Seguí AE, Lerma JL - Sensors (Basel) (2012)

Bottom Line: A practical, comprehensive and flexible procedure to analyze the radiometric values and the uncertainty effects due to the camera sensor system is described in this paper.The presented linear model integrates all the individual sensor noise sources in one global component and characterizes the radiometric values and the uncertainty effects according to the influential factors such as the scene reflectance, wavelength range and time.It is confirmed the flexibility of the procedure to model and characterize the radiometric values, as well as to determine the behaviour of two phenomena when dealing with image sensors: the noise of a single image and the stability (trend and noise) of a sequence of images.

View Article: PubMed Central - PubMed

Affiliation: Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Valencia 46022, Spain. gabriuma@upv.es

ABSTRACT
Radiometric values on digital imagery are affected by several sources of uncertainty. A practical, comprehensive and flexible procedure to analyze the radiometric values and the uncertainty effects due to the camera sensor system is described in this paper. The procedure is performed on the grey level output signal using image raw units with digital numbers ranging from 0 to 2(12)-1. The procedure is entirely based on statistical and experimental techniques. Design of Experiments (DoE) for Linear Models (LM) are derived to analyze the radiometric values and estimate the uncertainty. The presented linear model integrates all the individual sensor noise sources in one global component and characterizes the radiometric values and the uncertainty effects according to the influential factors such as the scene reflectance, wavelength range and time. The experiments are carried out under laboratory conditions to minimize the rest of uncertainty sources that might affect the radiometric values. It is confirmed the flexibility of the procedure to model and characterize the radiometric values, as well as to determine the behaviour of two phenomena when dealing with image sensors: the noise of a single image and the stability (trend and noise) of a sequence of images.

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Related in: MedlinePlus

Goodness-of-fit graphic of the lineal model. Observed vs. adjusted radiometric values. It can be observed the linearity of the process. The separation with respect the straight line is the error or residual.
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f2-sensors-12-10339: Goodness-of-fit graphic of the lineal model. Observed vs. adjusted radiometric values. It can be observed the linearity of the process. The separation with respect the straight line is the error or residual.

Mentions: The resulting ANOVA table is shown in Table 3. All the effects included in the model are largely statistically significant (see P-Value column in the ANOVA table). The model has a goodness-of-fit of 99.97% (R-square in ANOVA table), Figure 2. This suggests that under uniform external conditions the imaging process can be modelled very accurately by a linear model. The variability of the model, i.e., residual variability, equals to the 0.03% of the total variability. Since all the influential factors affecting the experimental process are included in the model, the residual variability can be assigned to the sensor noise of the process of a sequence of images, which is composed of both the noise of a single image and the temporal non-uniformity random effects.


Grey level and noise evaluation of a Foveon X3 image sensor: a statistical and experimental approach.

Riutort-Mayol G, Marqués-Mateu A, Seguí AE, Lerma JL - Sensors (Basel) (2012)

Goodness-of-fit graphic of the lineal model. Observed vs. adjusted radiometric values. It can be observed the linearity of the process. The separation with respect the straight line is the error or residual.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472831&req=5

f2-sensors-12-10339: Goodness-of-fit graphic of the lineal model. Observed vs. adjusted radiometric values. It can be observed the linearity of the process. The separation with respect the straight line is the error or residual.
Mentions: The resulting ANOVA table is shown in Table 3. All the effects included in the model are largely statistically significant (see P-Value column in the ANOVA table). The model has a goodness-of-fit of 99.97% (R-square in ANOVA table), Figure 2. This suggests that under uniform external conditions the imaging process can be modelled very accurately by a linear model. The variability of the model, i.e., residual variability, equals to the 0.03% of the total variability. Since all the influential factors affecting the experimental process are included in the model, the residual variability can be assigned to the sensor noise of the process of a sequence of images, which is composed of both the noise of a single image and the temporal non-uniformity random effects.

Bottom Line: A practical, comprehensive and flexible procedure to analyze the radiometric values and the uncertainty effects due to the camera sensor system is described in this paper.The presented linear model integrates all the individual sensor noise sources in one global component and characterizes the radiometric values and the uncertainty effects according to the influential factors such as the scene reflectance, wavelength range and time.It is confirmed the flexibility of the procedure to model and characterize the radiometric values, as well as to determine the behaviour of two phenomena when dealing with image sensors: the noise of a single image and the stability (trend and noise) of a sequence of images.

View Article: PubMed Central - PubMed

Affiliation: Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Valencia 46022, Spain. gabriuma@upv.es

ABSTRACT
Radiometric values on digital imagery are affected by several sources of uncertainty. A practical, comprehensive and flexible procedure to analyze the radiometric values and the uncertainty effects due to the camera sensor system is described in this paper. The procedure is performed on the grey level output signal using image raw units with digital numbers ranging from 0 to 2(12)-1. The procedure is entirely based on statistical and experimental techniques. Design of Experiments (DoE) for Linear Models (LM) are derived to analyze the radiometric values and estimate the uncertainty. The presented linear model integrates all the individual sensor noise sources in one global component and characterizes the radiometric values and the uncertainty effects according to the influential factors such as the scene reflectance, wavelength range and time. The experiments are carried out under laboratory conditions to minimize the rest of uncertainty sources that might affect the radiometric values. It is confirmed the flexibility of the procedure to model and characterize the radiometric values, as well as to determine the behaviour of two phenomena when dealing with image sensors: the noise of a single image and the stability (trend and noise) of a sequence of images.

No MeSH data available.


Related in: MedlinePlus