scholarly journals COMPARISON OF ULTRASOUND IMAGE FILTERING METHODS BY MEANS OF MULTIVARIABLE KURTOSIS

2017 ◽  
Vol 36 (2) ◽  
pp. 79 ◽  
Author(s):  
Mariusz Nieniewski ◽  
Paweł Zajączkowski
Keyword(s):  
Image Quality ◽  
Real Life ◽  
Ultrasound Image ◽  
Numerical Procedure ◽  
General Purpose ◽  
Reference Image ◽  
Image Metrics ◽  
Multivariate Kurtosis ◽  
Golden Standard

Comparison of the quality of despeckled US medical images is complicated because there is no image of a human body that would be free of speckles and could serve as a reference. A number of various image metrics are currently used for comparison of filtering methods; however, they do not satisfactorily represent the visual quality of images and medical expert’s satisfaction with images. This paper proposes an innovative use of relative multivariate kurtosis for the evaluation of the most important edges in an image. Multivariate kurtosis allows one to introduce an order among the filtered images and can be used as one of the metrics for image quality evaluation. At present there is no method which would jointly consider individual metrics. Furthermore, these metrics are typically defined by comparing the noisy original and filtered images, which is incorrect since the noisy original cannot serve as a golden standard. In contrast to this, the proposed kurtosis is the absolute measure, which is calculated independently of any reference image and it agrees with the medical expert’s satisfaction to a large extent. The paper presents a numerical procedure for calculating kurtosis and describes results of such calculations for a computer-generated noisy image, images of a general purpose phantom and a cyst phantom, as well as real-life images of thyroid and carotid artery obtained with SonixTouch ultrasound machine. 16 different methods of image despeckling are compared via kurtosis. The paper shows that visually more satisfactory despeckling results are associated with higher kurtosis, and to a certain degree kurtosis can be used as a single metric for evaluation of image quality. 

Denoising Ultrasound Medical Images

2017 ◽  
pp. 761-775
Author(s):  
A.S.C.S. Sastry ◽  
P.V.V. Kishore ◽  
Ch. Raghava Prasad ◽  
M.V.D. Prasad
Keyword(s):  
Image Quality ◽  
Medical Images ◽  
Ultrasound Image ◽  
Similarity Index ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Soft Thresholding ◽  
Block Based

Medical ultrasound imaging has revolutioned the diagnostics of human body in the last few decades. The major drawback of ultrasound medical images is speckle noise. Speckle noise in ultrasound images is because of multiple reflections of ultrasound waves from hard tissues. Speckle noise degrades the medical ultrasound images lessening the visible quality of the image. The aim of this paper is to improve the image quality of ultrasound medical images by applying block based hard and soft thresholding on wavelet coefficients. Medical ultrasound image transformation to wavelet domain uses debauchee's mother wavelet. Divide the approximate and detailed coefficients into uniform blocks of size 8×8, 16×16, 32×32 and 64×64. Hard and soft thresholding on these blocks of approximate and detailed coefficients reduces speckle noise. Inverse transformation to original spatial domain produces a noise reduced ultrasound image. Experiments on medical ultrasound images obtained from diagnostic centers in Vijayawada, India show good improvements to ultrasound images visually. Quality of improved images in measured using peak signal to noise ratio (PSNR), image quality index (IQI), structural similarity index (SSIM).

scholarly journals Underwater No-Reference Image Quality Assessment for Display Module of ROV

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Di Wu ◽  
Fei Yuan ◽  
En Cheng
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Water Medium ◽  
Display Module ◽  
Optical Images ◽  
Underwater Image ◽  
Imaging Characteristics ◽  
Monitor Image

The optical images collected by remotely operated vehicles (ROV) contain a lot of information about underwater (such as distributions of underwater creatures and minerals), which plays an important role in ocean exploration. However, due to the absorption and scattering characteristics of the water medium, some of the images suffer from serious color distortion. These distorted color images usually need to be enhanced so that we can analyze them further. However, at present, no image enhancement algorithm performs well in any scene. Therefore, in order to monitor image quality in the display module of ROV, a no-reference image quality predictor (NIPQ) is proposed in this paper. A unique property that differentiates the proposed NIPQ metric from existing works is the consideration of the viewing behavior of the human visual system and imaging characteristics of the underwater image in different water types. The experimental results based on the underwater optical image quality database (UOQ) show that the proposed metric can provide an accurate prediction for the quality of the enhanced image.

scholarly journals DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6457
Author(s):  
Hayat Ullah ◽  
Muhammad Irfan ◽  
Kyungjin Han ◽  
Jong Weon Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Reference Image ◽  
Primary Concern ◽  
Panoramic Images ◽  
Stitching Errors

Due to recent advancements in virtual reality (VR) and augmented reality (AR), the demand for high quality immersive contents is a primary concern for production companies and consumers. Similarly, the topical record-breaking performance of deep learning in various domains of artificial intelligence has extended the attention of researchers to contribute to different fields of computer vision. To ensure the quality of immersive media contents using these advanced deep learning technologies, several learning based Stitched Image Quality Assessment methods have been proposed with reasonable performances. However, these methods are unable to localize, segment, and extract the stitching errors in panoramic images. Further, these methods used computationally complex procedures for quality assessment of panoramic images. With these motivations, in this paper, we propose a novel three-fold Deep Learning based No-Reference Stitched Image Quality Assessment (DLNR-SIQA) approach to evaluate the quality of immersive contents. In the first fold, we fined-tuned the state-of-the-art Mask R-CNN (Regional Convolutional Neural Network) on manually annotated various stitching error-based cropped images from the two publicly available datasets. In the second fold, we segment and localize various stitching errors present in the immersive contents. Finally, based on the distorted regions present in the immersive contents, we measured the overall quality of the stitched images. Unlike existing methods that only measure the quality of the images using deep features, our proposed method can efficiently segment and localize stitching errors and estimate the image quality by investigating segmented regions. We also carried out extensive qualitative and quantitative comparison with full reference image quality assessment (FR-IQA) and no reference image quality assessment (NR-IQA) on two publicly available datasets, where the proposed system outperformed the existing state-of-the-art techniques.

A SPATIAL DOMAIN OBJECT SEPARABILITY BASED NO-REFERENCE IMAGE QUALITY MEASURE USING MEAN AND VARIANCE

2013 ◽  
Vol 13 (02) ◽  
pp. 1340005 ◽  
Author(s):  
KANJAR DE ◽  
V. MASILAMANI
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Quality Measure ◽  
Reference Image ◽  
Individual Object ◽  
The Difference ◽  
Image Quality Measure ◽  
Good Quality Image ◽  
Degraded Images

In many modern image processing applications determining quality of the image is one of the most challenging tasks. Researchers working in the field of image quality assessment design algorithms for measuring and quantifying image quality. The human eye can identify the difference between a good quality image and a noisy image by simply looking at the image, but designing a computer algorithm to automatically determine the quality of an image is a very challenging task. In this paper, we propose an image quality measure using the concept of object separability. We define object separability using variance. Two objects are very well separated if variance of individual object is less and mean pixel values of neighboring objects are very different. Degradation in images can be due to a number of reasons like additive noises, quantization defects, sampling defects, etc. The proposed no-reference image quality measure will determine quality of degraded images and differentiate between good and degraded images.

scholarly journals NO-REFERENCE IMAGE QUALITY ASSESSMENT FOR ZY3 IMAGERY IN URBAN AREAS USING STATISTICAL MODEL

2016 ◽  
Vol XLI-B3 ◽  
pp. 949-954 ◽  
Author(s):  
Y. Zhang ◽  
W.H. Cui ◽  
F. Yang ◽  
Z.C. Wu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Urban Areas ◽  
Satellite Images ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Atmospheric Conditions ◽  
Natural Scene Statistics ◽  
The Impact

More and more high-spatial resolution satellite images are produced with the improvement of satellite technology. However, the quality of images is not always satisfactory for application. Due to the impact of complicated atmospheric conditions and complex radiation transmission process in imaging process the images often suffer deterioration. In order to assess the quality of remote sensing images over urban areas, we proposed a general purpose image quality assessment methods based on feature extraction and machine learning. We use two types of features in multi scales. One is from the shape of histogram the other is from the natural scene statistics based on Generalized Gaussian distribution (GGD). A 20-D feature vector for each scale is extracted and is assumed to capture the RS image quality degradation characteristics. We use SVM to learn to predict image quality scores from these features. In order to do the evaluation, we construct a median scale dataset for training and testing with subjects taking part in to give the human opinions of degraded images. We use ZY3 satellite images over Wuhan area (a city in China) to conduct experiments. Experimental results show the correlation of the predicted scores and the subjective perceptions.

scholarly journals Extended Damage Detection and Identification in Aircraft Structure Based on Multifrequency Eddy Current Method and Mutual Image Similarity Assessment

Materials ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4452
Author(s):  
Tomasz Chady ◽  
Krzysztof Okarma ◽  
Robert Mikołajczyk ◽  
Michał Dziendzikowski ◽  
Piotr Synaszko ◽  
...  
Keyword(s):  
Image Quality ◽  
Eddy Current ◽  
Image Quality Assessment ◽  
Current Method ◽  
General Purpose ◽  
Reference Image ◽  
Destructive Testing ◽  
Eddy Current Method ◽  
Detection And Identification ◽  
Novel Approach

In this paper, a novel approach to Non-Destructive Testing (NDT) of defective materials for the aircraft industry is proposed, which utilizes an approach based on multifrequency and spectrogram eddy current method combined with an image analysis method previously applied for general-purpose full-reference image quality assessment (FR IQA). The proposed defect identification method is based on the use of the modified SSIM4 image quality metric. The developed method was thoroughly tested for various locations, sizes, and configurations of defects in the examined structure. Its application makes it possible to not only determine the presence of cracks but also estimate their size.

scholarly journals A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 313
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Feature Vector ◽  
State Of The Art ◽  
Prediction Performance ◽  
Perceptual Quality ◽  
Reference Image ◽  
Full Reference

The goal of full-reference image quality assessment (FR-IQA) is to predict the perceptual quality of an image as perceived by human observers using its pristine (distortion free) reference counterpart. In this study, we explore a novel, combined approach which predicts the perceptual quality of a distorted image by compiling a feature vector from convolutional activation maps. More specifically, a reference-distorted image pair is run through a pretrained convolutional neural network and the activation maps are compared with a traditional image similarity metric. Subsequently, the resulting feature vector is mapped onto perceptual quality scores with the help of a trained support vector regressor. A detailed parameter study is also presented in which the design choices of the proposed method is explained. Furthermore, we study the relationship between the amount of training images and the prediction performance. Specifically, it is demonstrated that the proposed method can be trained with a small amount of data to reach high prediction performance. Our best proposal—called ActMapFeat—is compared to the state-of-the-art on six publicly available benchmark IQA databases, such as KADID-10k, TID2013, TID2008, MDID, CSIQ, and VCL-FER. Specifically, our method is able to significantly outperform the state-of-the-art on these benchmark databases.

scholarly journals Entropy-Based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1525
Author(s):  
Krzysztof Okarma ◽  
Wojciech Chlewicki ◽  
Mateusz Kopytek ◽  
Beata Marciniak ◽  
Vladimir Lukin
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Subjective Evaluation ◽  
General Purpose ◽  
Sensing Applications ◽  
Objective Quality ◽  
Panoramic Images ◽  
Remote Sensing Applications

Quality assessment of stitched images is an important element of many virtual reality and remote sensing applications where the panoramic images may be used as a background as well as for navigation purposes. The quality of stitched images may be decreased by several factors, including geometric distortions, ghosting, blurring, and color distortions. Nevertheless, the specificity of such distortions is different than those typical for general-purpose image quality assessment. Therefore, the necessity of the development of new objective image quality metrics for such type of emerging applications becomes obvious. The method proposed in the paper is based on the combination of features used in some recently proposed metrics with the results of the local and global image entropy analysis. The results obtained applying the proposed combined metric have been verified using the ISIQA database, containing 264 stitched images of 26 scenes together with the respective subjective Mean Opinion Scores, leading to a significant increase of its correlation with subjective evaluation results.

scholarly journals NO-REFERENCE IMAGE QUALITY MEASURE FOR IMAGES WITH MULTIPLE DISTORTIONS USING RANDOM FORESTS FOR MULTI METHOD FUSION

2018 ◽  
Vol 37 (2) ◽  
pp. 105 ◽  
Author(s):  
Kanjar De ◽  
Masilamani V
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Multiple Sources ◽  
Noise Image ◽  
Assessment Techniques ◽  
Image Quality Measure ◽  
Channel Errors

Over the years image quality assessment is one of the active area of research in image processing. Distortion in images can be caused by various sources like noise, blur, transmission channel errors, compression artifacts etc. Image distortions can occur during the image acquisition process (blur/noise), image compression (ringing and blocking artifacts) or during the transmission process. A single image can be distorted by multiple sources and assessing quality of such images is an extremely challenging task. The human visual system can easily identify image quality in such cases, but for a computer algorithm performing the task of quality assessment is a very difficult. In this paper, we propose a new no-reference image quality assessment for images corrupted by more than one type of distortions. The proposed technique is compared with the best-known framework for image quality assessment for multiply distorted images and standard state of the art Full reference and No-reference image quality assessment techniques available. 

Analysis and Evaluation of Image Fusion Algorithms

2015 ◽  
Vol 6 (2) ◽  
Author(s):  
Neeraj Kumar ◽  
Vikas Kumar Mishra ◽  
C. L.P. Gupta
Keyword(s):  
Image Quality ◽  
Image Fusion ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Imaging Systems ◽  
Reference Image ◽  
Analysis And Evaluation ◽  
Reference Methods ◽  
Objectively Measured

There is an increasing need for performance tools or quality assessment in order to compare the results obtained with different algorithms of image fusion. This analysis can be used to select a specific algorithm for a defined fusion dataset. The image quality is a characteristic of an image that measures the perceived image degradation (typically, compared to an ideal or perfect picture). Imaging systems may introduce a certain amount of distortion or artifacts in the signal, hence the quality assessment is an important problem. There are several techniques and measures that can be objectively measured and evaluated automatically by a computer program. Therefore, they may be classified as complete reference methods (FR) and the No-reference methods (NR). In the methods of image quality assessment FR, the quality of a test image is evaluated by comparing a reference image that is supposed to have perfect quality. NR measures attempt to assess the quality of an image without any reference to the original.

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