Blind image quality assessment for Gaussian blur images using exact Zernike moments and gradient magnitude

Lim, Chern-Loon; Raveendran, P.; Jassim, Wissam A.; Yu, Yong-Poh; Ngan, King Ngi

doi:10.1016/j.jfranklin.2016.08.012

Cited by 20 publications

(9 citation statements)

References 37 publications

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“…Color, edge, and structural information is the technique used to discriminate images with different levels of blur in [ 22 ]. Exact Zernite moments which reflects human visual characteristics was extracted from test images in [ 23 ]. The exact Zernite moments are combined with contrast information from gradient magnitude to measure the level of blur in the image.…”

Section: Introductionmentioning

confidence: 99%

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Blind blur assessment of MRI images using parallel multiscale difference of Gaussian filters

et al. 2018

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BackgroundRician noise, bias fields and blur are the common distortions that degrade MRI images during acquisition. Blur is unique in comparison to Rician noise and bias fields because it can be introduced into an image beyond the acquisition stage such as postacquisition processing and the manifestation of pathological conditions. Most current blur assessment algorithms are designed and validated on consumer electronics such as television, video and mobile appliances. The few algorithms dedicated to medical images either requires a reference image or incorporate manual approach. For these reasons it is difficult to compare quality measures from different images and images with different contents. Furthermore, they will not be suitable in environments where large volumes of images are processed. In this report we propose a new blind blur assessment method for different types of MRI images and for different applications including automated environments.MethodsTwo copies of the test image are generated. Edge map is extracted by separately convolving each copy of the test image with two parallel difference of Gaussian filters. At the start of the multiscale representation, the initial output of the filters are equal. In subsequent scales of the multiscale representation, each filter is tuned to different operating parameters over the same fixed range of Gaussian scales. The filters are termed low and high energy filters based on their characteristics to successively attenuate and highlight edges over the range of multiscale representation. Quality score is predicted from the distance between the normalized mean of the edge maps at the final output of the filters.ResultsThe proposed method was evaluated on cardiac and brain MRI images. Performance evaluation shows that the quality index has very good correlation with human perception and will be suitable for application in routine clinical practice and clinical research.

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Section: Introductionmentioning

confidence: 99%

“…One of the few contributions focused on medical images is the edge sharpness assessment by parametric (ESAP) modeling [ 23 ]. Sharpness assessment in ESAP begins with manual selection of region of interest from edge map extracted from the test image.…”

Section: Introductionmentioning

confidence: 99%

Blind blur assessment of MRI images using parallel multiscale difference of Gaussian filters

et al. 2018

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“…IVL MD [57], [58], MDID2013 [46] and MDID [59] datasets and for Bokeh mode images IBBI database [16], [60] are used. The methods considered for comparisons, include distortion specific such as Ferzli [9], EZMGM [15], Golestaneh [22], Zhan [23], Zhu [24], H. Liu [16] as well as general purpose methods such as DIIVINE [31], BLIINDS2 [32], CORNIA [38], BRISQUE [29], GMLoG [33], NFERM [34], FRIQUEE [37], BJLC [39], NIQE [41] and IL-NIQE [42]. Some methods designed for multiply distorted images namely GWH-GLBP [44], Miao [45], Zhou [47] and SISBLIM [46] specifically, are also included for fair comparison.…”

Section: Resultsmentioning

confidence: 99%

“…It is a two-step framework in which first the image is filtered by a Gaussian low pass filter (GLPF) and then the corresponding changes in histogram are analyzed to determine extent of blurriness. In [15], authors have used the Exact Zernike Moments & Gradient Magnitude (EZMGM) for establishing a blurriness metric. H. Liu et al have proposed a blur metric in [16] which utilizes edge blur and neural network and works well on images having intentionally blurred background.…”

Section: Related Workmentioning

confidence: 99%

Frequency-domain blind quality assessment of blurred and blocking-artefact images using Gaussian Process Regression model

Viqar

Moinuddin

Khan

et al. 2022

Signal Processing: Image Communication

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“…So in this paper, the eddy current pulsed thermography is implemented in the detection of defects of pneumatic pressure equipment. Researchers also have made valuable contributions in processing data [12][13][14][15], which can be applied in (NDT&E). P. Zhu et al presented an improved feature extraction algorithm for automatic defect identification based on eddy current pulsed thermography [16].…”

Section: Introductionmentioning

confidence: 99%

Design of an Automatic Defect Identification Method Based ECPT for Pneumatic Pressure Equipment

et al. 2018

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In this paper, in order to achieve automatic defect identification for pneumatic pressure equipment, an improved feature extraction algorithm eddy current pulsed thermography (ECPT) is presented. The presented feature extraction algorithm contains four elements: data block selection; variable step search; relation value classification; and between-class distance decision function. The data block selection and variable step search are integrated to decrease the redundant computations in the automatic defect identification. The goal of the classification and between-class distance calculation is to select the typical features of thermographic sequence. The main image information can be extracted by the method precisely and efficiently. Experimental results are provided to demonstrate the capabilities and benefits (i.e., reducing the processing time) of the proposed algorithm in automatic defect identification.

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Blind image quality assessment for Gaussian blur images using exact Zernike moments and gradient magnitude

Cited by 20 publications

References 37 publications

Blind blur assessment of MRI images using parallel multiscale difference of Gaussian filters

Blind blur assessment of MRI images using parallel multiscale difference of Gaussian filters

Frequency-domain blind quality assessment of blurred and blocking-artefact images using Gaussian Process Regression model

Design of an Automatic Defect Identification Method Based ECPT for Pneumatic Pressure Equipment

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