No-reference blur image quality measure based on multiplicative multiresolution decomposition

Serir, Amina; Beghdadi, Azeddine; Kerouh, Fatma

doi:10.1016/j.jvcir.2013.06.002

Cited by 18 publications

(5 citation statements)

References 29 publications

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“…For the objective image quality evaluation method, usually feature information in the original reference image according to need how many can be divided into the following three categories: full reference, reference and no reference image quality evaluation method of the reference and the reference to be on the basis of the original reference image evaluation method is used to compress image quality evaluation, and not for forging measurement for reference of the original image; As for the nonreference evaluation method, its biggest advantage is that it does not need any original reference image information [3]. However, this method still has the problem that it is difficult to guarantee the accuracy and reliability of evaluation, and it requires the establishment of corresponding evaluation model for different image features to be measured to realize the reliable evaluation of image quality.…”

Section: Objective Image Quality Evaluation Methodsmentioning

confidence: 99%

An Image Quality Evaluation Model for Optical Strip Signal-to-Noise Ratio in the Target Area of High Temperature Forgings

Ma¹,

Zhao²,

Feng³

et al. 2021

J Multimed Inf Syst

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Under the time-varying temperature, the high-temperature radiation of forgings and the change of reflection characteristics of oxide skin on the surface of forgings lead to the difficulty of obtaining images to truly reflect the geometric characteristics of forgings. It is urgent to study the clear and reliable acquisition method of hot forging feature image under time-varying temperature to meet the requirements of visual measurement of hot geometric parameters of forgings. Based on this, this chapter first puts forward the quality evaluation method of forging feature image, which provides guarantee for the accurate evaluation of feature image quality. Furthermore, the factors that affect the image quality, such as the radiation characteristics of forgings and the photographic characteristics of cameras, are analyzed, and the imaging spectrum which can effectively suppress the radiation intensity of forgings is determined. Finally, aiming at the problem that the quality of image acquisition is difficult to guarantee due to the drastic change of radiation intensity of forgings under time-varying temperature, an image acquisition method based on minimum signal-to-noise ratio (SNR) based laser light intensity adaptation is proposed, which significantly improves the definition of feature light strips in forging images at high temperature, and finally realizes the clear acquisition of feature images of large-scale hot forging under time-varying temperature.

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Section: Objective Image Quality Evaluation Methodsmentioning

confidence: 99%

An Image Quality Evaluation Model for Optical Strip Signal-to-Noise Ratio in the Target Area of High Temperature Forgings

Ma¹,

Zhao²,

Feng³

et al. 2021

J Multimed Inf Syst

View full text Add to dashboard Cite

show abstract

“…NR-IQA models can be divided into two categories, such as distortion-specific and general methods. Specifically, distortion-specific methods assume a certain distortion type (e.g., blur [6] or JPEG compression noise [7]) present in the image, while general algorithms do not make any assumptions about the noise or distortion type present in the image. Early NR-IQA methods mainly fell into the distortion-specific category.…”

Section: Related Workmentioning

confidence: 99%

No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion

Varga

2021

Applied Sciences

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No-reference image quality assessment (NR-IQA) has always been a difficult research problem because digital images may suffer very diverse types of distortions and their contents are extremely various. Moreover, IQA is also a very hot topic in the research community since the number and role of digital images in everyday life is continuously growing. Recently, a huge amount of effort has been devoted to exploiting convolutional neural networks and other deep learning techniques for no-reference image quality assessment. Since deep learning relies on a massive amount of labeled data, utilizing pretrained networks has become very popular in the literature. In this study, we introduce a novel, deep learning-based NR-IQA architecture that relies on the decision fusion of multiple image quality scores coming from different types of convolutional neural networks. The main idea behind this scheme is that a diverse set of different types of networks is able to better characterize authentic image distortions than a single network. The experimental results show that our method can effectively estimate perceptual image quality on four large IQA benchmark databases containing either authentic or artificial distortions. These results are also confirmed in significance and cross database tests.

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“…The image is decomposed into an approximate output subband y 1 and three detail images y 2h , y 2v and y 2d characterized by horizontal, vertical and diagonal directions. The desired structure is obtained by performing a polyphase decomposition of the 2D signal (the image) (Serir et al 2013). To avoid division by zero, the value 1 is added to the original image to provide the input image (I).…”

Section: Multiplicative Decompositionmentioning

confidence: 99%

Subjective and objective evaluations of feature selected multi output filter for speckle reduction on ultrasound images

Outtas¹,

Zhang²,

Déforges³

et al. 2018

Phys. Med. Biol.

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Ultrasonographic examination, either as visual inspection or quantitative analysis, is the most widely diagnostic resource. However, speckle noise is one of the drawbacks that makes it less effective than other medical imaging systems. Several speckle reduction methods often offer effective speckle reduction but generally suffer from oversmoothing, a blurring effect and a man-made appearance. In this paper, we propose a multi-output filter based on the multiplicative multiresolution decomposition (MOF-MMD). This multi-scale based method enables the enhancement of the original image and provides three enhanced outputs: global, edge and texture images. The multi-output filter aims at offering an enhanced image according to the features desired by radiologists. The different structures, textures and edges are filtered according to the contour image obtained by morphological operators. Three radiologists with different years of experience, have subjectively evaluated the speckle reduction methods according to enhanced features. The results of objective metrics and subjective evaluation showed that the proposed method reduces speckle and could help radiologists, according to their years of experience, in the diagnostic task. Finally, the correlation between three objective metrics and the perceived quality of contrast, diagnostic, texture and edges show that an objective metric is suitable for assessing quality of ultrasound images.

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No-reference blur image quality measure based on multiplicative multiresolution decomposition

Cited by 18 publications

References 29 publications

An Image Quality Evaluation Model for Optical Strip Signal-to-Noise Ratio in the Target Area of High Temperature Forgings

An Image Quality Evaluation Model for Optical Strip Signal-to-Noise Ratio in the Target Area of High Temperature Forgings

No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion

Subjective and objective evaluations of feature selected multi output filter for speckle reduction on ultrasound images

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