481,234 research outputs found
Application of the CIE color spaces for the digital image quality assessment
The digital image quality assessment is one of the most relevant aspects of contemporary digital image processing. A rapid development of some modern quality assessment techniques in recent years has caused the introduction of some new metrics, much better correlated with the Human Visual System (HVS) than the traditional ones such as the Mean Squared Error (MSE) or PSNR (Peak Signal to Noise Ratio). One of the most popular modern image quality assessment techniques is the usage of the Structural Similarity index (SSIM) defined in 2004. Unfortunately, even some modern image quality metrics are usually defined for the grayscale images so the colour information is often ignored. A typical classical approach to the quality assessment of the color images is the use of the Normalized Color Difference calculated in the CIE L*a*b* ob CIE Lu'v' colour space but its correlation with the human assessment is rather poor. In the paper the analysis of the influence of using the color spaces recommended by the CIE on the results of the digital image quality assessment using some modern metrics is performed. All the results have been calculated for the widely known LIVE database (release 2) containing the Differential Mean Opinion Scores (DMOS) for nearly 1000 color images with five types of distortions: JPEG compression, JPEG2000 compression, Gaussian blur, white noise and transmission over the simulated fast fading Rayleigh channel typical of wireless transmissions. As the final result the comparison of the correlations between the DMOS values and the SSIM metric calculated for various colour spaces recommended by the CIE is presented
Comparison of modern nonlinear multichannel filtering techniques using recent full-reference image quality assessment methods
In the paper the quality analysis of some modern nonlinear color image filtering methods is presented. Traditionally, many image filtering algorithms are analyzed using classical image quality assessment metrics, mainly based on the Mean Square Error (MSE). However, they are all poorly correlated with subjective evaluation of images performed by observers.Due to necessity of better image quality estimation, some other methods have been recently proposed. They are especially useful for development of new lossy image compression algorithms, as well as evaluation of images obtained after applying some image processing algorithms e.g. filtering methods.Most of image quality algorithms are based on the comparison of similarity between two images: the original (reference) one and the second one which is processed e.g. contaminated by noise, filtered or lossily compressed. Such a group of full-reference methods is actually the only existing universal solution for automatic image quality assessment. There are also some blind (no-reference) algorithms but they are specialized for some kinds of distortions e.g. blocky effects in the JPEG compressed images. The last years' state-of-the-art full-reference metrics are Structural Similarity (SSIM) and M-SVD based on the Singular Value Decomposition of two images' respective blocks.Another important aspect of color image quality assessment is the way the color information is utilized in the quality metric. The authors of two analyzed metrics generally do not consider the effects of using color information at all or limit the usage of their metrics to luminance information in YUV color model only so in this article the solutions based on RGB and CIE LAB models are compared.In the paper the results of quality assessment using the SSIM and M-SVD methods obtained for some modern median-based filters and Distance-Directional Filter for color images are presented with comparison to those obtained using classical metrics as the verification of their usefulness
Comparison of modern nonlinear multichannel filtering techniques using recent full-reference image quality assessment methods
In the paper the quality analysis of some modern nonlinear color image filtering methods is presented. Traditionally, many image filtering algorithms are analyzed using classical image quality assessment metrics, mainly based on the Mean Square Error (MSE). However, they are all poorly correlated with subjective evaluation of images performed by observers.Due to necessity of better image quality estimation, some other methods have been recently proposed. They are especially useful for development of new lossy image compression algorithms, as well as evaluation of images obtained after applying some image processing algorithms e.g. filtering methods.Most of image quality algorithms are based on the comparison of similarity between two images: the original (reference) one and the second one which is processed e.g. contaminated by noise, filtered or lossily compressed. Such a group of full-reference methods is actually the only existing universal solution for automatic image quality assessment. There are also some blind (no-reference) algorithms but they are specialized for some kinds of distortions e.g. blocky effects in the JPEG compressed images. The last years' state-of-the-art full-reference metrics are Structural Similarity (SSIM) and M-SVD based on the Singular Value Decomposition of two images' respective blocks.Another important aspect of color image quality assessment is the way the color information is utilized in the quality metric. The authors of two analyzed metrics generally do not consider the effects of using color information at all or limit the usage of their metrics to luminance information in YUV color model only so in this article the solutions based on RGB and CIE LAB models are compared.In the paper the results of quality assessment using the SSIM and M-SVD methods obtained for some modern median-based filters and Distance-Directional Filter for color images are presented with comparison to those obtained using classical metrics as the verification of their usefulness
Assessing Bias in Face Image Quality Assessment
Face image quality assessment (FIQA) attempts to improve face recognition
(FR) performance by providing additional information about sample quality.
Because FIQA methods attempt to estimate the utility of a sample for face
recognition, it is reasonable to assume that these methods are heavily
influenced by the underlying face recognition system. Although modern face
recognition systems are known to perform well, several studies have found that
such systems often exhibit problems with demographic bias. It is therefore
likely that such problems are also present with FIQA techniques. To investigate
the demographic biases associated with FIQA approaches, this paper presents a
comprehensive study involving a variety of quality assessment methods
(general-purpose image quality assessment, supervised face quality assessment,
and unsupervised face quality assessment methods) and three diverse
state-of-theart FR models. Our analysis on the Balanced Faces in the Wild (BFW)
dataset shows that all techniques considered are affected more by variations in
race than sex. While the general-purpose image quality assessment methods
appear to be less biased with respect to the two demographic factors
considered, the supervised and unsupervised face image quality assessment
methods both show strong bias with a tendency to favor white individuals (of
either sex). In addition, we found that methods that are less racially biased
perform worse overall. This suggests that the observed bias in FIQA methods is
to a significant extent related to the underlying face recognition system.Comment: The content of this paper was published in EUSIPCO 202
Multiscale Astronomical Image Processing Based on Nonlinear Partial Differential Equations
Astronomical applications of recent advances in the field of nonastronomical image processing are presented. These innovative methods, applied to multiscale astronomical images, increase signal-to-noise ratio, do not smear point sources or extended diffuse structures, and are thus a highly useful preliminary step for detection of different features including point sources, smoothing of clumpy data, and removal of contaminants from background maps. We show how the new methods, combined with other algorithms of image processing, unveil fine diffuse structures while at the same time enhance detection of localized objects, thus facilitating interactive morphology studies and paving the way for the automated recognition and classification of different features. We have also developed a new application framework for astronomical image processing that implements some recent advances made in computer vision and modern image processing, along with original algorithms based on nonlinear partial differential equations. The framework enables the user to easily set up and customize an image-processing pipeline interactively; it has various common and new visualization features and provides access to many astronomy data archives. Altogether, the results presented here demonstrate the first implementation of a novel synergistic approach based on integration of image processing, image visualization, and image quality assessment
Intensity Adjustment and Noise Removal for Medical Image Enhancement
Introduction: Image contrast enhancement is an image processing method in which the output image
has high quality display. Medical images have prominent role in modern diagnosis; therefore, this
study aimed to enhance the quality of medical images in order to help radiologists and surgeons in
finding abnormal areas.
Method: The methods used in this study to enhance medical images quality are categorized into two
groups; intensity adjustment and noise removal. Intensity adjustment methods including techniques for
mapping image intensity values to the new domain. The second group including methods to remove
noise from the images. Medical images used in this study including images of spine, brain, lung and
breast.
Results: The results were analyzed based on five criteria including the number of detected edges,
PCNR, Image Quality Index, AMBE and visual quality that the number of detected edges in images of
spine, brain, lungs and breast were 6465, 10305, 16266 and 13509, respectively.
Conclusion: The results show that the methods with intensity adjustment technique have better
performance in criteria such as the number of detected edges and image visual assessment. However,
the other method include in noise removal technique perform more effectively in PCNR, Image
Quality Index and AMBE measure
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
In this study, we explore quantitative correlates of qualitative human expert
perception. We discover that current quality metrics and loss functions,
considered for biomedical image segmentation tasks, correlate moderately with
segmentation quality assessment by experts, especially for small yet clinically
relevant structures, such as the enhancing tumor in brain glioma. We propose a
method employing classical statistics and experimental psychology to create
complementary compound loss functions for modern deep learning methods, towards
achieving a better fit with human quality assessment. When training a CNN for
delineating adult brain tumor in MR images, all four proposed loss candidates
outperform the established baselines on the clinically important and hardest to
segment enhancing tumor label, while maintaining performance for other label
channels
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
In this study, we explore quantitative correlates of qualitative human expert perception. We discover that current quality metrics and loss functions, considered for biomedical image segmentation tasks, correlate moderately with segmentation quality assessment by experts, especially for small yet clinically relevant structures, such as the enhancing tumor in brain glioma. We propose a method employing classical statistics and experimental psychology to create complementary compound loss functions for modern deep learning methods, towards achieving a better fit with human quality assessment. When training a CNN for delineating adult brain tumor in MR images, all four proposed loss candidates outperform the established baselines on the clinically important and hardest to segment enhancing tumor label, while maintaining performance for other label channels
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