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One-to-One Mapping-like Properties of DCN-Based Super-Resolution and its Applicability to Real-World Images

C. Lee, J. Yoon, J. Kim, S. Park
2021 IEEE Access  
Then, we applied several SRDNN techniques to real-world images and analyzed the output images.  ...  Since most SRDNN techniques can be viewed as dynamic linear projections, we analyzed a large number of projection vectors (over 70 million) and found that the SRDNN method performs one-to-one mapping-like  ...  However, to provide robust performance for real-world compressed images, new structures may be necessary for successful SRDNN applications.  ... 
doi:10.1109/access.2021.3108396 fatcat:m4a5lxsa6bbdzemnhfjupze3aa

Compression Artifacts Reduction by a Deep Convolutional Network [article]

Chao Dong and Yubin Deng and Chen Change Loy and Xiaoou Tang
2015 arXiv   pre-print
Inspired by the deep convolutional networks (DCN) on super-resolution, we formulate a compact and efficient network for seamless attenuation of different compression artifacts.  ...  Our method shows superior performance than the state-of-the-arts both on the benchmark datasets and the real-world use case (i.e. Twitter).  ...  [4] shows the great potential of an end-to-end DCN in image super-resolution.  ... 
arXiv:1504.06993v1 fatcat:l5xktogq3zgqfmhzw3qr45yrpq

Multiscale Spatial Fusion and Regularization Induced Unsupervised Auxiliary Task CNN Model for Deep Super-Resolution of Hyperspectral Images

Viet Khanh Ha, Jinchang Ren, Zheng Wang, Genyun Sun, Huimin Zhao, Stephen Marshall
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
As such, various methods have been developed for enhancing the spatial resolution of the low-resolution HSI (Lr-HSI) by fusing it with high-resolution multispectral images (Hr-MSI).  ...  In this article, a multiscale spatial fusion and regularization induced auxiliary task based convolutional neural network model is proposed for deep super-resolution of HSI, where an Lr-HSI is fused with  ...  ACKNOWLEDGMENT The authors would like to thank colleagues from the Image Processing Group in Strathclyde University for their valuable suggestions.  ... 
doi:10.1109/jstars.2022.3176969 fatcat:gv5zgotyrncaph6qb4kd43sczm

Compression Artifacts Reduction by a Deep Convolutional Network

Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang
2015 2015 IEEE International Conference on Computer Vision (ICCV)  
Inspired by the deep convolutional networks (DCN) on super-resolution [5], we formulate a compact and efficient network for seamless attenuation of different compression artifacts.  ...  Our method shows superior performance than the state-of-the-arts both on the benchmark datasets and the real-world use case (i.e. Twitter).  ...  [5] shows the great potential of an end-to-end DCN in image super-resolution.  ... 
doi:10.1109/iccv.2015.73 dblp:conf/iccv/DongDLT15 fatcat:mhmq5hysbvdqlgervxufdowsje

Deep Convolution Networks for Compression Artifacts Reduction [article]

Ke Yu, Chao Dong, Chen Change Loy, Xiaoou Tang
2016 arXiv   pre-print
To meet the speed requirement of real-world applications, we further accelerate the proposed baseline model by layer decomposition and joint use of large-stride convolutional and deconvolutional layers  ...  Our method shows superior performance than the state-of-the-art methods both on benchmark datasets and a real-world use case.  ...  [6] shows the great potential of an end-toend DCN in image super-resolution.  ... 
arXiv:1608.02778v1 fatcat:okb4eqezabcgzif3yxbszwdq74

All One Needs to Know about Priors for Deep Image Restoration and Enhancement: A Survey [article]

Yunfan Lu, Yiqi Lin, Hao Wu, Yunhao Luo, Xu Zheng, Lin Wang
2022 arXiv   pre-print
methods; (3) An insightful discussion on each prior regarding its principle, potential, and applications; (4) A summary of crucial problems by highlighting the potential future directions to spark more  ...  Due to its ill-posed property, plenty of works have explored priors to facilitate training deep neural networks (DNNs).  ...  in the image storage and transmission process [10] ; (5) super-resolution (SR), which aims to enhance the resolution of an image or video [11] ; (6) video frame interpolation, a.k.a., super slow  ... 
arXiv:2206.02070v1 fatcat:icu7hwua3jggbp7owl2l5mgyfu

Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: A comprehensive review

Lubna Aziz, Sah bin Haji Salam, Sara Ayub
2020 IEEE Access  
Object detection is a fundamental but challenging issue in the field of generic image analysis; it plays an important role in a wide range of applications and has been receiving special attention in recent  ...  We mainly focus on the application of deep learning architectures to five major applications, namely Object Detection in Surveillance, Military, Transportation, Medical, and Daily Life.  ...  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.  ... 
doi:10.1109/access.2020.3021508 fatcat:guri46oiejhfzeitxuuprpmjka

Visual information processing for deep-sea visual monitoring system

Chunyan Ma, Xin Li, Yujie Li, Xinliang Tian, Yichuan Wang, Hyoungseop Kim, Seiichi Serikawa
2021 Cognitive Robotics  
Due to the rising demand for minerals and metals, various deep-sea mining systems have been developed for the detection of mines and mine-like objects on the seabed.  ...  In this paper, we propose the concept of a learning-based deep-sea visual monitoring system and use testbeds to show the efficiency of the proposed system.  ...  The offset of the key points is used as the offset of the DCN to stitch the original feature maps to keep the features consistent with the target area.  ... 
doi:10.1016/j.cogr.2020.12.002 fatcat:dvgtme5rzve4hhejbf66ct6azq

A New Dataset and Transformer for Stereoscopic Video Super-Resolution [article]

Hassan Imani, Md Baharul Islam, Lai-Kuan Wong
2022 arXiv   pre-print
There are several notable works on stereoscopic image super-resolution, but there is little research on stereo video super-resolution.  ...  Stereo video super-resolution (SVSR) aims to enhance the spatial resolution of the low-resolution video by reconstructing the high-resolution video.  ...  SVSR-Set Dataset Due to the lack of an existing real-world dataset that is sufficiently large for training a good deep neural network model for the SVSR task, we developed SVSR-Set, a new real-world dataset  ... 
arXiv:2204.10039v1 fatcat:jrtzadwh75evxczx5vgx4dc4am

A Survey of Deep Learning-based Object Detection

Licheng Jiao, Fan Zhang, Fang Liu, Shuyuan Yang, Lingling Li, Zhixi Feng, Rong Qu
2019 IEEE Access  
Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with  ...  Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art  ...  The chip detection network based on FPGA will make real-time application possible. 12) MEDICAL IMAGING AND DIAGNOSIS FDA (U.S.  ... 
doi:10.1109/access.2019.2939201 fatcat:jesz2av2tjbkxfpaqyecptgls4

Research Contribution and Comprehensive Review towards the Semantic Segmentation of Aerial Images Using Deep Learning Techniques

P. Anilkumar, P. Venugopal, Mamoun Alazab
2022 Security and Communication Networks  
The main aim of this review is to provide a clear algorithmic categorization and analysis of the diverse contribution of semantic segmentation of aerial images and expects to give the comprehensive details  ...  and remote-sensing images.  ...  Also, the authors would like to thank the individual copyright holders for consent conceded to incorporate referred figures in this work. 26 Security and Communication Networks  ... 
doi:10.1155/2022/6010912 fatcat:qxoogfb3zneypkh5w3m5p3ts3e

Deep Learning for Generic Object Detection: A Survey

Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, Matti Pietikäinen
2019 International Journal of Computer Vision  
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images.  ...  Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection.  ...  The authors would like to thank the pioneering researchers in generic object detection and other related fields.  ... 
doi:10.1007/s11263-019-01247-4 fatcat:isdmz4febvbthgowo33c6ifhm4

Modeling Target Detection and Performance Analysis of Electronic Countermeasures for Phased Radar

T. Jagadesh, B. Sheela Rani
2023 Intelligent Automation and Soft Computing  
Strong interference might make it difficult to detect the signal or targets.  ...  When interference occurs in the sidelobes of the antenna pattern, Sidelobe Cancellation (SLC) and Sidelobe Blanking are two unique solutions to solve this problem (SLB).  ...  [13] introduced a super-resolution (SR) reconstruction approach based on an effective subpixel CNN.  ... 
doi:10.32604/iasc.2023.026868 fatcat:b47gk2dptjfbhm3nhbztdm4nmy

Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience [article]

Rongguang Wang, Vishnu Bashyam, Zhijian Yang, Fanyang Yu, Vasiliki Tassopoulou, Lasya P. Sreepada, Sai Spandana Chintapalli, Dushyant Sahoo, Ioanna Skampardoni, Konstantina Nikita, Ahmed Abdulkadir, Junhao Wen (+1 others)
2022 arXiv   pre-print
This review appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis  ...  We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging  ...  Acknowledgment This work was supported by the National Institute on Aging (grant numbers RF1AG054409 and U01AG068057), the National Institute of Mental Health (grant number R01MH112070), the National Cancer  ... 
arXiv:2206.07081v1 fatcat:463hupobs5fp7okuguhp6tmzia

A survey of deep neural network architectures and their applications

Weibo Liu, Zidong Wang, Xiaohui Liu, Nianyin Zeng, Yurong Liu, Fuad E. Alsaadi
2017 Neurocomputing  
To construct a standard neural network (NN), it is essential to utilize neurons to produce real-valued activations and, by adjusting the weights, the NNs behave as expected.  ...  Both Google and Baidu have updated their image search engines based on Hinton's deep learning architecture with great improvements in searching accuracy.  ...  Due to their visualized contents and high resolution properties, spaceborne images are superior to other remote sensing images in object detection.  ... 
doi:10.1016/j.neucom.2016.12.038 fatcat:nkxvbhp47rfflpi5jev7hk4yq4
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