Non-reference assessment of sharpness in blur/noise degraded images

Ospina-Borras, J. E.; Restrepo, Hernán Darío Benitez

doi:10.1016/j.jvcir.2016.05.015

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…The assessment methods of image sharpness are divided into three types: full reference type, semi-reference type and non-reference type [19,20,21]. The nonreference sharpness assessment method is to assess an indicator of an image to be evaluated, such as the degree of blurring, without using the original image as a reference [21].…”

Section: Sharpness Assessment Of Hologramsmentioning

confidence: 99%

Study on Holographic Image Recognition Technology of Zooplankton

Shi¹,

Wang²,

Cao³

et al. 2019

dtcse

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Marine zooplankton has important ecological and economic value. The observation and automatic image recognition technology of marine zooplankton is an important mean to acquire data such as species, quantity, spatial distribution and behavioral postures of zooplankton, and is an important support for marine scientific research. Digital holography has an innate advantage of refocusing and reconstruction, which is suitable for deep learning and living zooplankton recognition. In this study, a large number of holographic images was trained by using the improved YOLOv2 model, and after test, the study achieved satisfactory results: the models trained by the images with sharpness assessment score of 0.6 or higher, have precision rate above 94% and a recall rate above 88%. This study mainly discusses: (1) the detection method of moving targets to acquire the images of moving zooplankton; (2) the two factors that affect the holographic images recognition results, mean (pixel mean of images) subtraction operation and image sharpness, and the no-reference sharpness assessment based on structural similarity for holographic images; (3) the relationship between sharpness assessment index or mean subtraction and the recognition results.

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Section: Sharpness Assessment Of Hologramsmentioning

confidence: 99%

Study on Holographic Image Recognition Technology of Zooplankton

Shi¹,

Wang²,

Cao³

et al. 2019

dtcse

View full text Add to dashboard Cite

show abstract

“…Then, the NIQE index is expressed as the distance between the interest IR image MVG model and the pristine MVG model. 13 Similar to NIQE, the model of our proposed metric, which is based on the gradient of singular values decomposition (GSVD), can be computed easily finding the mode and maximum of the edge energy changes that represent the quality of the image, 14 and fitting them with Gaussian Mixture Model (GMM). The mixing coefficient, mean and covariance matrix parameters of the GMM are computed using the expectation maximization algorithm (EM).…”

Section: Non-reference Iqa Metrics Trainingmentioning

confidence: 99%

Non-reference quality assessment of infrared images reconstructed by compressive sensing

Ospina-Borras

Benítez-Restrepo

2015

SPIE Proceedings

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Infrared (IR) images are representations of the world and have natural features like images in the visible spectrum. As such, natural features from infrared images support image quality assessment (IQA).. 1 In this work, we compare the quality of a set of indoor and outdoor IR images reconstructed from measurement functions formed by linear combination of their pixels. The reconstruction methods are: linear discrete cosine transform (DCT) acquisition, DCT augmented with total variation minimization, and compressive sensing scheme. Peak Signal to Noise Ratio (PSNR), three full-reference (FR), and four no-reference (NR) IQA measures compute the qualities of each reconstruction: multi-scale structural similarity (MSSIM), visual information fidelity (VIF), information fidelity criterion (IFC), sharpness identification based on local phase coherence (LPC-SI), blind/referenceless image spatial quality evaluator (BRISQUE), naturalness image quality evaluator (NIQE) and gradient singular value decomposition (GSVD), respectively. Each measure is compared to human scores that were obtained by differential mean opinion score (DMOS) test. We observe that GSVD has the highest correlation coefficients of all NR measures, but all FR have better performance. We use MSSIM to compare the reconstruction methods and we find that CS scheme produces a good-quality IR image, using only 30000 random sub-samples and 1000 DCT coefficients (2%). In contrast, linear DCT provides higher correlation coefficients than CS scheme by using all the pixels of the image and 31000 DCT (47%) coefficients.

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“…In this work, the discussion is confined to NR approaches, which are considered challenging and highly desired due to their applicability in absence of reference images. Many measures are devoted to evaluating the perceptual quality of images distorted by Gaussian white noise, JPEG compression, contrast change, or Gaussian blur [17,18]. Since their practical application is limited and based on the prior knowledge of distortion types, general-purpose NR methods have been developed.…”

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confidence: 99%

No-Reference Image Quality Assessment with Local Gradient Orientations

Oszust

2019

Symmetry

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Image processing methods often introduce distortions, which affect the way an image is subjectively perceived by a human observer. To avoid inconvenient subjective tests in cases in which reference images are not available, it is desirable to develop an automatic no-reference image quality assessment (NR-IQA) technique. In this paper, a novel NR-IQA technique is proposed in which the distributions of local gradient orientations in image regions of different sizes are used to characterize an image. To evaluate the objective quality of an image, its luminance and chrominance channels are processed, as well as their high-order derivatives. Finally, statistics of used perceptual features are mapped to subjective scores by the support vector regression (SVR) technique. The extensive experimental evaluation on six popular IQA benchmark datasets reveals that the proposed technique is highly correlated with subjective scores and outperforms related state-of-the-art hand-crafted and deep learning approaches.Symmetry 2019, 11, 95 2 of 20 patches [28] can be found in the literature.Since the supervised learning bridges image statistics with the perceptual quality, it is often applied to obtain a model used for the quality prediction. For the learning, the Support Vector Regression (SVR) [20,21,23,27], neural networks [24], or random forests [29] are applied. In methods that do not use supervised learning, distortion types are modeled with a set of centroids of quality levels or NSS from multiple cues [30,31]. Another direction is to employ a pseudo-reference image which is created and compared with a distorted image with blockiness, sharpness, and noisiness metrics [32]. Recently, many NR-IQA approaches which use deep neural network (DNN) architectures have been introduced. They merge the feature extraction and quality prediction steps. However, they suffer from a small number of training examples available in IQA benchmark datasets or use complex architectures that are devoted to image recognition tasks. To overcome these limitations, most of them use image patches [33,34], train models using FR-IQA measures instead of subjective scores [34,35], or perform fine-tuning to adopt an architecture to the IQA [36]. Interestingly, some DNN-based approaches use features introduced in earlier methods [35].The HVS is sensitive to local structures, which are often described using local binary patterns (LBP) and gradient-based statistics. However, a spatial distribution of LBP may not be able to capture more complex structures [37]. Thus, statistics extracted from gradient maps often occur in conjunction with other approaches to improve the IQA performance [27,38]. Such techniques use global distributions of gradient magnitude maps [25], relative gradient orientations or magnitude [24,39].To describe an image and efficiently take into account local gradient orientations, Histogram of Oriented Gradients (HOG) descriptor can be used [40]. However, the HOG produces high-dimensional feature vectors, which are devoted to object recognition...

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Non-reference assessment of sharpness in blur/noise degraded images

Cited by 8 publications

References 20 publications

Study on Holographic Image Recognition Technology of Zooplankton

Study on Holographic Image Recognition Technology of Zooplankton

Non-reference quality assessment of infrared images reconstructed by compressive sensing

No-Reference Image Quality Assessment with Local Gradient Orientations

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