9 research outputs found
Out-of-focus Blur: Image De-blurring
Image de-blurring is important in many cases of imaging a real scene or
object by a camera. This project focuses on de-blurring an image distorted by
an out-of-focus blur through a simulation study. A pseudo-inverse filter is
first explored but it fails because of severe noise amplification. Then
Tikhonov regularization methods are employed, which produce greatly improved
results compared to the pseudo-inverse filter. In Tikhonov regularization, the
choice of the regularization parameter plays a critical rule in obtaining a
high-quality image, and the regularized solutions possess a semi-convergence
property. The best result, with the relative restoration error of 8.49%, is
achieved when the prescribed discrepancy principle is used to decide an optimal
value. Furthermore, an iterative method, Conjugated Gradient, is employed for
image de-blurring, which is fast in computation and leads to an even better
result with the relative restoration error of 8.22%. The number of iteration in
CG acts as a regularization parameter, and the iterates have a semi-convergence
property as well.Comment: 11 page
High-density lipoprotein-cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides and high-sensitivity C-reactive protein (hsCRP) in male controls (white bars), swimmers (grey bars) and basketball players (black bars).
<p>* indicates difference between the groups (p<0.05).</p
Means and standard deviations by group and results of ANOVA to test the effect of sport participation on DXA assessments on trunk, upper limbs and lower limbs.
<p>Means and standard deviations by group and results of ANOVA to test the effect of sport participation on DXA assessments on trunk, upper limbs and lower limbs.</p
Whole body bone mineral content (BMC), trunk BMC, upper limbs BMC, lower limbs BMC in male controls (white bars), swimmers (grey bars) and basketball players (black bars) adjusted by chorological age, maturity offset, vitamin D intake and weekly training load.
<p>* indicates difference between the groups (p<0.05).</p
Descriptive statistics for variables reporting inter-individual variability on DXA assessments on trunk, upper limbs and lower limbs (n = 53).
<p>Descriptive statistics for variables reporting inter-individual variability on DXA assessments on trunk, upper limbs and lower limbs (n = 53).</p
Mean difference between groups on DXA assessments on trunk, upper limbs and lower limbs.
<p>Mean difference between groups on DXA assessments on trunk, upper limbs and lower limbs.</p
Descriptive statistics on chronological age, maturation, training experience, parameters of training experience, indicators of lipid profile plus inflammatory biomarker, anthropometry of the overall body size and outputs of whole body DXA assessments for the total sample (n = 53).
<p>Descriptive statistics on chronological age, maturation, training experience, parameters of training experience, indicators of lipid profile plus inflammatory biomarker, anthropometry of the overall body size and outputs of whole body DXA assessments for the total sample (n = 53).</p
Means and standard deviations by group and results of ANOVA to test the effect of sport participation on chronological age, maturation, training experience, indicators of lipid profile plus inflammatory biomarker, anthropometry of the overall body size and outputs of whole body DXA assessments.
<p>Means and standard deviations by group and results of ANOVA to test the effect of sport participation on chronological age, maturation, training experience, indicators of lipid profile plus inflammatory biomarker, anthropometry of the overall body size and outputs of whole body DXA assessments.</p
Mean difference between groups on chronological age, maturation, training experience, parameters of training load, anthropometry of the overall body size, indicators of lipid profile plus inflammatory biomarker and outputs of whole body DXA.
<p>Mean difference between groups on chronological age, maturation, training experience, parameters of training load, anthropometry of the overall body size, indicators of lipid profile plus inflammatory biomarker and outputs of whole body DXA.</p