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A shallow convolutional neural network for blind image sharpness assessment

Shaode Yu, Shibin Wu, Lei Wang, Fan Jiang, Yaoqin Xie, Leida Li, You Yang
2017 PLoS ONE  
It necessitates considerable expertise and efforts to handcraft features for optimal representation of perceptual image quality.  ...  Blind image quality assessment can be modeled as feature extraction followed by score prediction.  ...  Acknowledgments The authors would like to thank reviewers for their valuable advices that has helped to improve the paper quality.  ... 
doi:10.1371/journal.pone.0176632 pmid:28459832 pmcid:PMC5436206 fatcat:u5ceycuxhzfohm3uuis3ztmn7e

Blind Quality Assessment for Image Superresolution Using Deep Two-Stream Convolutional Networks [article]

Wei Zhou, Qiuping Jiang, Yuwang Wang, Zhibo Chen, Weiping Li
2020 arXiv   pre-print
In this paper, we propose a no-reference/blind deep neural network-based SR image quality assessor (DeepSRQ).  ...  To learn more discriminative feature representations of various distorted SR images, the proposed DeepSRQ is a two-stream convolutional network including two subcomponents for distorted structure and texture  ...  In Section II, we introduce the proposed deep neural network-based SR image quality assessor (DeepSRQ) for noreference/blind superresolution image quality prediction in detail.  ... 
arXiv:2004.06163v1 fatcat:yckfryo6lra2pciektyxdf4aiu

A Comparative Study of DNN-Based Models for Blind Image Quality Prediction

Xiaohan Yang, Fan Li, Hantao Liu
2019 2019 IEEE International Conference on Image Processing (ICIP)  
Recently, deep learning methods have gained substantial attention in the research community and have proven useful for blind image quality assessment (BIQA).  ...  Index Termsdeep learning, blind image quality assessment (BIQA), deep neural networks (DNN)  ...  Therefore, methods for image quality assessment (IQA) have been extensively studied for the purpose of maintain, control and enhance the perceived image quality.  ... 
doi:10.1109/icip.2019.8804268 dblp:conf/icip/Yang0L19 fatcat:rw6uoylrtrefdbwlqccarqph5e

Multi-context Deep Network for Angle-Closure Glaucoma Screening in Anterior Segment OCT [chapter]

Huazhu Fu, Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, Baskaran Mani, Meenakshi Mahesh, Tin Aung, Jiang Liu
2018 Lecture Notes in Computer Science  
These successes have motivated our examination of deep learning for glaucoma assessment in AS-OCT imagery.  ...  These methods, however, lack sufficiently discriminative representations and are easily affected by noise and low quality of the AS-OCT image.  ...  A possible reason for this is that although learned discriminative features are more powerful than handcrafted visual features, they are learned in this case over the entire AS-OCT image.  ... 
doi:10.1007/978-3-030-00934-2_40 fatcat:wlywqrjhhrd57oikowunokbc7q

Deep Blind Synthesized Image Quality Assessment with Contextual Multi-Level Feature Pooling

Xiaochuan Wang, Kai Wang, Bailin Yang, Frederick W.B. Li, Xiaohui Liang
2019 2019 IEEE International Conference on Image Processing (ICIP)  
Index Termsimage quality assessment, synthesized image, feature pooling, DIBR, deep learning  ...  2019) 'Deep blind wynthesized image quality assessment with contextual multi-level feature pooling. any current or future media, including reprinting/republishing this material for advertising or promotional  ...  Despite of its success in general computer vision tasks, applying deep learning to blind synthesized image quality assessment still encounters difficulties [4] .  ... 
doi:10.1109/icip.2019.8802943 dblp:conf/icip/WangWYLL19 fatcat:upqnvxcjefevvfdyucqqikdtdy

Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

Smita Khade, Swati Ahirrao, Shraddha Phansalkar, Ketan Kotecha, Shilpa Gite, Sudeep D. Thepade
2021 Inventions  
Many works were restricted to handcrafted methods of feature extraction, which are confronted with bigger feature sizes.  ...  Iris-based authentication offers stronger, unique, and contactless identification of the user.  ...  This review was restricted to techniques such as Machine Learning-based Handcrafted Feature Extraction and Deep Learning-based Self-Learned features.  ... 
doi:10.3390/inventions6040065 fatcat:v5hj2jyu3rbbzdyo3jdvue5fbi

Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database Training [article]

Wei Sun and Xiongkuo Min and Guangtao Zhai and Siwei Ma
2021 arXiv   pre-print
Image quality assessment (IQA) is very important for both end-users and service-providers since a high-quality image can significantly improve the user's quality of experience (QoE) and also benefit lots  ...  Most existing blind image quality assessment (BIQA) models were developed for synthetically distorted images, however, they perform poorly on in-the-wild images, which are widely existed in various practical  ...  Blind Image Quality Assessment Based on the methods of feature extraction, BIQA models can be divided into two categories: handcrafted feature based and learning feature based.  ... 
arXiv:2105.14550v2 fatcat:o3tbulnjobgaph4xbye6hlkury

Evolution, current challenges, and future possibilities in the objective assessment of aesthetic outcome of breast cancer locoregional treatment

Jaime S. Cardoso, Wilson Silva, Maria J. Cardoso
2020 Breast  
Recent advancements in machine learning have inspired trends toward deep-learning-based medical image analysis, also bringing new promises to the field of aesthetic assessment of locoregional treatments  ...  This increase in survival paralleled the awareness over the long-lasting impact of the side effects of treatments on patient quality of life, emphasizing the motto "a longer but better life for breast  ...  The algorithm learns, directly from the image, to compute features and to use those features in the analysis of the aesthetic result.  ... 
doi:10.1016/j.breast.2019.11.006 pmid:31790958 pmcid:PMC7375658 fatcat:xwuo3vw46bf3le5ya3edk4v3vm

A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment [article]

Ghalib Tahir, Chu Kiong Loo
2021 arXiv   pre-print
First, we will present the rationale of visual-based methods for food recognition.  ...  Recent dietary monitoring systems tackle these challenges by automatic assessment of dietary intake through machine learning methods.  ...  Some of these methods implement a combination of handcrafted and deep visual features for image feature representation.  ... 
arXiv:2106.11776v3 fatcat:kockhutzizdyplcxikcffyid4a

A Multi-task convolutional neural network for blind stereoscopic image quality assessment using naturalness analysis [article]

Salima Bourbia, Ayoub Karine, Aladine Chetouani, Mohammed El Hassouni
2021 arXiv   pre-print
This paper addresses the problem of blind stereoscopic image quality assessment (NR-SIQA) using a new multi-task deep learning based-method.  ...  Our method is based on two main tasks: the first task predicts naturalness analysis based features adapted to stereo images, while the second task predicts the quality of such images.  ...  PROPOSED DEEP-BASED BLIND STEREO IMAGE QUALITY ASSESSMENT METHOD Fig. 1 presents the flowchart of the proposed method which has two major stages: Binocular feature extraction and multitask prediction  ... 
arXiv:2106.09303v3 fatcat:ugvkgljqorasnpbbgo7bkk6qc4

UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content [article]

Zhengzhong Tu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik
2021 arXiv   pre-print
Accordingly, there is a great need for accurate video quality assessment (VQA) models for UGC/consumer videos to monitor, control, and optimize this vast content.  ...  Our study protocol also defines a reliable benchmark for the UGC-VQA problem, which we believe will facilitate further research on deep learning-based VQA modeling, as well as perceptually-optimized efficient  ...  Tu years of research on the topics of perceptual video and image quality assessment (VQA/IQA).  ... 
arXiv:2005.14354v2 fatcat:bjixvu7ryza4vglncr6v23eal4

A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment

Ghalib Ahmed Tahir, Chu Kiong Loo
2021 Healthcare  
Our findings indicate that around 66.7% of surveyed studies use visual features from deep neural networks for food recognition.  ...  Then, the core of the study is the presentation, discussion, and evaluation of these methods based on popular food image databases.  ...  Conflicts of Interest: The authors wish to confirm that there are no conflicts of interest.  ... 
doi:10.3390/healthcare9121676 pmid:34946400 pmcid:PMC8700885 fatcat:aeq6xfascfhwzjlokulmjlhxve

Intelligent Glioma Grading Based on Deep Transfer Learning of MRI Radiomic Features

Chung-Ming Lo, Yu-Chih Chen, Rui-Cian Weng, Kevin Li-Chun Hsieh
2019 Applied Sciences  
image features, the DCNN without pretrained features, which only achieved a mean accuracy of 61.42% with a standard deviation of ±7% and a mean Az of 0.8222 ± 0.07, and the DCNN without data augmentation  ...  Gliomas from a multi-center database (The Cancer Imaging Archive) composed of a total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were used for the training and evaluation of the proposed CAD.  ...  -AE1-B46 and 106TMU-TMUH-20) for financially supporting this research.  ... 
doi:10.3390/app9224926 fatcat:u7ii4t3uazazbeyhfpu2hbm4ya

Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images [article]

Debapriya Maji, Anirban Santara, Pabitra Mitra, Debdoot Sheet
2016 arXiv   pre-print
In this work we present a computational imaging framework using deep and ensemble learning for reliable detection of blood vessels in fundus color images.  ...  An ensemble of deep convolutional neural networks is trained to segment vessel and non-vessel areas of a color fundus image.  ...  of abnormalities, and (iii) quality quantification of images acquired to assess reporting fitness [5] .  ... 
arXiv:1603.04833v1 fatcat:vjawokqq4vh5jitezjqlzi3jay

Angle-Closure Detection in Anterior Segment OCT Based on Multilevel Deep Network

Huazhu Fu, Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, Mani Baskaran, Meenakshi Mahesh, Tin Aung, Jiang Liu
2019 IEEE Transactions on Cybernetics  
In this paper, an automated system based on deep learning is presented for angle-closure detection in AS-OCT images.  ...  Our system learns a discriminative representation from training data that captures subtle visual cues not modeled by handcrafted features.  ...  The deep learning based methods can learn rich representations more powerful than clinical parameters and handcrafted visual features.  ... 
doi:10.1109/tcyb.2019.2897162 pmid:30794201 fatcat:h4fjkt5uffdkdoqtwxsfv75uve
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