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TFP-YOLO: Obstacle and Traffic Sign Detection for Assisting Visually Impaired Pedestrians. [PDF]

Sensors (Basel)
Zheng Z, Cheng J, Jin F.
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The Development of Audio-Tactile Spatial Integration: Unraveling Vision's Contribution. [PDF]

Dev Sci
Tonelli A, Senna I, Amadeo MB, Setti W, Domenici N, Signorini S, Cocchi E, Giammari G, Strazzer S, Tinelli F, Camicione P, Serafino M, Gori M.   +12 more
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Deep Blind Hyperspectral Image Super-Resolution

IEEE Transactions on Neural Networks and Learning Systems, 2021
The production of a high spatial resolution (HR) hyperspectral image (HSI) through the fusion of a low spatial resolution (LR) HSI with an HR multispectral image (MSI) has underpinned much of the recent progress in HSI super-resolution. The premise of these signs of progress is that both the degeneration from the HR HSI to LR HSI in the spatial domain ...
Lei Zhang, Jiangtao Nie, Wei Wei, Yong Li, Yanning Zhang   +4 more
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Nonparametric Blind Super-resolution

2013 IEEE International Conference on Computer Vision, 2013
Super resolution (SR) algorithms typically assume that the blur kernel is known (either the Point Spread Function 'PSF' of the camera, or some default low-pass filter, e.g. a Gaussian). However, the performance of SR methods significantly deteriorates when the assumed blur kernel deviates from the true one.
Tomer Michaeli, Michal Irani
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Non-stationary blind super-resolution

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
In this paper, we propose a new framework for parameter estimation of complex exponentials from their modulations with unknown waveforms via convex programming. Our model generalizes the recently developed blind sparse spike deconvolution framework by Y. Chi [1] to the non-stationary scenario and encompasses a wide spectrum of applications.
Dehui Yang, Gongguo Tang, Michael B. Wakin   +2 more
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MAP Based Blind Super-Resolution

2012 International Conference on Industrial Control and Electronics Engineering, 2012
Super-resolution is the process of obtaining a high resolution image from multiple low resolution images. In most of the super-resolution algorithms, the blur parameter of a LR-image model always have to be manually set as a default value, this is not a good solution.
Liu Gang, Hu Zhenlong
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Patch based blind image super resolution

Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005
In this paper, a novel method for learning based image super resolution (SR) is presented. The basic idea is to bridge the gap between a set of low resolution (LR) images and the corresponding high resolution (HR) image using both the SR reconstruction constraint and a patch based image synthesis constraint in a general probabilistic framework. We show
null Qiang Wang, null Xiaoou Tang, H. Shum   +2 more
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