6,914 research outputs found
Statistical evaluation of visual quality metrics for image denoising
This paper studies the problem of full reference visual quality assessment of
denoised images with a special emphasis on images with low contrast and
noise-like texture. Denoising of such images together with noise removal often
results in image details loss or smoothing. A new test image database, FLT,
containing 75 noise-free "reference" images and 300 filtered ("distorted")
images is developed. Each reference image, corrupted by an additive white
Gaussian noise, is denoised by the BM3D filter with four different values of
threshold parameter (four levels of noise suppression). After carrying out a
perceptual quality assessment of distorted images, the mean opinion scores
(MOS) are obtained and compared with the values of known full reference quality
metrics. As a result, the Spearman Rank Order Correlation Coefficient (SROCC)
between PSNR values and MOS has a value close to zero, and SROCC between values
of known full-reference image visual quality metrics and MOS does not exceed
0.82 (which is reached by a new visual quality metric proposed in this paper).
The FLT dataset is more complex than earlier datasets used for assessment of
visual quality for image denoising. Thus, it can be effectively used to design
new image visual quality metrics for image denoising.Comment: Submitted to ICASSP 201
A statistical reduced-reference method for color image quality assessment
Although color is a fundamental feature of human visual perception, it has
been largely unexplored in the reduced-reference (RR) image quality assessment
(IQA) schemes. In this paper, we propose a natural scene statistic (NSS)
method, which efficiently uses this information. It is based on the statistical
deviation between the steerable pyramid coefficients of the reference color
image and the degraded one. We propose and analyze the multivariate generalized
Gaussian distribution (MGGD) to model the underlying statistics. In order to
quantify the degradation, we develop and evaluate two measures based
respectively on the Geodesic distance between two MGGDs and on the closed-form
of the Kullback Leibler divergence. We performed an extensive evaluation of
both metrics in various color spaces (RGB, HSV, CIELAB and YCrCb) using the TID
2008 benchmark and the FRTV Phase I validation process. Experimental results
demonstrate the effectiveness of the proposed framework to achieve a good
consistency with human visual perception. Furthermore, the best configuration
is obtained with CIELAB color space associated to KLD deviation measure
Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI
Background: Prostate cancer is one of the most common forms of cancer found
in males making early diagnosis important. Magnetic resonance imaging (MRI) has
been useful in visualizing and localizing tumor candidates and with the use of
endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The
coils introduce intensity inhomogeneities and the surface coil intensity
correction built into MRI scanners is used to reduce these inhomogeneities.
However, the correction typically performed at the MRI scanner level leads to
noise amplification and noise level variations. Methods: In this study, we
introduce a new Monte Carlo-based noise compensation approach for coil
intensity corrected endorectal MRI which allows for effective noise
compensation and preservation of details within the prostate. The approach
accounts for the ERC SNR profile via a spatially-adaptive noise model for
correcting non-stationary noise variations. Such a method is useful
particularly for improving the image quality of coil intensity corrected
endorectal MRI data performed at the MRI scanner level and when the original
raw data is not available. Results: SNR and contrast-to-noise ratio (CNR)
analysis in patient experiments demonstrate an average improvement of 11.7 dB
and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong
performance when compared to existing approaches. Conclusions: A new noise
compensation method was developed for the purpose of improving the quality of
coil intensity corrected endorectal MRI data performed at the MRI scanner
level. We illustrate that promising noise compensation performance can be
achieved for the proposed approach, which is particularly important for
processing coil intensity corrected endorectal MRI data performed at the MRI
scanner level and when the original raw data is not available.Comment: 23 page
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