A shallow convolutional neural network for blind image sharpness assessment

Yu, Shaode; Wu, Shibin; Wang, Lei; Jiang, Fan; Xie, Yaoqin; Li, Leida

doi:10.1371/journal.pone.0176632

Cited by 47 publications

(36 citation statements)

References 67 publications

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“…To evaluate the proposed HVS-MaxPol 3 , we conduct experiments in terms of statistical correlation accuracies, computational complexity and scalability. All of these evaluation 2 FocusPath database https://sites.google.com/view/focuspathuoft 3 https://github.com/mahdihosseini/HVS-MaxPol [20] and Synthetic-MaxPol [25].…”

Section: Methodsmentioning

confidence: 99%

Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment

Hosseini

Zhang

Plataniotis

2019

IEEE Trans. on Image Process.

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In this paper, we propose a novel design of Human Visual System (HVS) response in a convolutional filter form to decompose meaningful features that are closely tied with image sharpness level. No-reference (NR) Image sharpness assessment (ISA) techniques have emerged as the standard of image quality assessment in diverse imaging applications. Despite their high correlation with subjective scoring, they are challenging for practical considerations due to high computational cost and lack of scalability across different image blurs. We bridge this gap by synthesizing the HVS response as a linear combination of Finite Impulse Response (FIR) derivative filters to boost the falloff of high band frequency magnitudes in natural imaging paradigm. The numerical implementation of the HVS filter is carried out with MaxPol filter library that can be arbitrarily set for any differential orders and cutoff frequencies to balance out the estimation of informative features and noise sensitivities. Utilized by HVS filter, we then design an innovative NR-ISA metric called "HVS-MaxPol" that (a) requires minimal computational cost, (b) produce high correlation accuracy with image sharpness level, and (c) scales to assess synthetic and natural image blur. Specifically, the synthetic blur images are constructed by blurring the raw images using Gaussian filter, while natural blur is observed from real-life application such as motion, out-offocus, luminance contrast, etc. Furthermore, we create a natural benchmark database in digital pathology for validation of image focus quality in whole slide imaging systems called "FocusPath" consisting of 864 blurred images. Thorough experiments are designed to test and validate the efficiency of HVS-MaxPol across different blur databases and state-of-the-art NR-ISA metrics. The experiment result indicates that our metric has the best overall performance with respect to speed, accuracy and scalability.

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Section: Methodsmentioning

confidence: 99%

Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment

Hosseini

Zhang

Plataniotis

2019

IEEE Trans. on Image Process.

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“…Following [13], the network was deeper and weights for patch scores were also integrated into the learning process [3]. In [45], CNN was used to learn features and the general regression neural network was used as the predictor. In [14], a sub-network was first trained on patches using the FR-IQA scores, and then a whole network from images to quality was trained.…”

Section: Learning-based Methodsmentioning

confidence: 99%

Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images

Jiang

2017

Proceedings of the 25th ACM International Conference on Multimedia

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To guarantee a satisfying Quality of Experience (QoE) for consumers, it is required to measure image quality efficiently and reliably.The neglect of the high-level semantic information may result in predicting a clear blue sky as bad quality, which is inconsistent with human perception. Therefore, in this paper, we tackle this problem by exploiting the high-level semantics and propose a novel no-reference image quality assessment method for realistic blur images. Firstly, the whole image is divided into multiple overlapping patches. Secondly, each patch is represented by the high-level feature extracted from the pre-trained deep convolutional neural network model. Thirdly, three different kinds of statistical structures are adopted to aggregate the information from different patches, which mainly contain some common statistics (i.e., the mean&standard deviation, quantiles and moments). Finally, the aggregated features are fed into a linear regression model to predict the image quality. Experiments show that, compared with low-level features, high-level features indeed play a more critical role in resolving the aforementioned challenging problem for quality estimation. Besides, the proposed method significantly outperforms the state-of-the-art methods on two realistic blur image databases and achieves comparable performance on two synthetic blur image databases.

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“…In discrete computational modeling, there exist several noreference (NR) focus quality assessment (FQA) (together called NR-FQA) metrics that can be applied in synthetic or natural imaging applications for sharpness assessment such as using gradient map [5]- [9], contrast map [10]- [12], phase coherency [13], [14], and deep learning solutions [15]- [17]. Please refer to [18] and the references therein for more information on the methodologies.…”

Section: Introductionmentioning

confidence: 99%

Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology

Hosseini

Brawley-Hayes

Zhang

et al. 2020

IEEE Trans. Med. Imaging

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One of the challenges facing the adoption of digital pathology workflows for clinical use is the need for automated quality control. As the scanners sometimes determine focus inaccurately, the resultant image blur deteriorates the scanned slide to the point of being unusable. Also, the scanned slide images tend to be extremely large when scanned at greater or equal 20X image resolution. Hence, for digital pathology to be clinically useful, it is necessary to use computational tools to quickly and accurately quantify the image focus quality and determine whether an image needs to be re-scanned. We propose a no-reference focus quality assessment metric specifically for digital pathology images, that operates by using a sum of evenderivative filter bases to synthesize a human visual system-like kernel, which is modeled as the inverse of the lens' point spread function. This kernel is then applied to a digital pathology image to modify high-frequency image information deteriorated by the scanner's optics and quantify the focus quality at the patch level. We show in several experiments that our method correlates better with ground-truth z-level data than other methods, and is more computationally efficient. We also extend our method to generate a local slide-level focus quality heatmap, which can be used for automated slide quality control, and demonstrate the utility of our method for clinical scan quality control by comparison with subjective slide quality scores.Index Terms-no-reference focus quality assessment, whole slide imaging, digital pathology, out-of-focus heatmap, human visual system, MaxPol derivative library Routine diagnosis of pathology slides requires high quality highthroughput images, which are directly affected by the dynamic environment of the physical optics and sensor electronics of the scanner [2]-[4].

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A shallow convolutional neural network for blind image sharpness assessment

Cited by 47 publications

References 67 publications

Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment

Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment

Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images

Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology

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