Real-Time Image Deblurring and Super Resolution Using Convolutional Neural Networks

2021 ◽  
pp. 381-394
Author(s):  
Nidhi Galgali ◽  
Melita Maria Pereira ◽  
N. K. Likitha ◽  
B. R. Madhushri ◽  
E. S. Vani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Image Deblurring ◽  
Real Time Image ◽  
Time Image

scholarly journals Low-Cost Implementation of Bilinear and Bicubic Image Interpolation for Real-Time Image Super-Resolution

2020 ◽  
Author(s):  
Donya Khaledyan ◽  
Abdolah Amirany ◽  
Kian Jafari ◽  
Mohammad Hossein Moaiyeri ◽  
Abolfazl Zargari Khuzani ◽  
...  
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Super Resolution ◽  
Image Interpolation ◽  
Real Time Image ◽  
Image Super Resolution ◽  
Time Image

Real-time image deblurring by optoelectronic hybrid processing

2011 ◽  
Vol 50 (33) ◽  
pp. 6184 ◽  
Author(s):  
Yixian Qian ◽  
Fangrong Hu ◽  
Xiaowei Cheng ◽  
Weimin Jin
Keyword(s):  
Real Time ◽  
Image Deblurring ◽  
Real Time Image ◽  
Time Image

scholarly journals A Homogeneous Algorithm for Motion Estimation and Compensation by Using Cellular Neural Networks

2010 ◽  
Vol 5 (5) ◽  
pp. 719 ◽  
Author(s):  
Cristian Grava ◽  
Alexandru Gacsádi ◽  
Ioan Buciu
Keyword(s):  
Neural Networks ◽  
Motion Estimation ◽  
Real Time ◽  
Cellular Neural Networks ◽  
Image Sequences ◽  
Artificial Vision ◽  
Real Time Image ◽  
Field Programmable ◽  
Real Time Applications ◽  
Time Image

In this paper we present an original implementation of a homogeneous algorithm for motion estimation and compensation in image sequences, by using Cellular Neural Networks (CNN). The CNN has been proven their efficiency in real-time image processing, because they can be implemented on a CNN chip or they can be emulated on Field Programmable Gate Array (FPGA). The motion information is obtained by using a CNN implementation of the well-known Horn & Schunck method. This information is further used in a CNN implementation of a motion-compensation method. Through our algorithm we obtain a homogeneous implementation for real-time applications in artificial vision or medical imaging. The algorithm is illustrated on some classical sequences and the results confirm the validity of our algorithm.

A Real-Time Super-Resolution Method Based on Convolutional Neural Networks

2019 ◽  
Vol 39 (2) ◽  
pp. 805-817 ◽  
Author(s):  
Shipeng Fu ◽  
Lu Lu ◽  
Hu Li ◽  
Zhen Li ◽  
Wei Wu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Resolution Method

Cost-Optimized Video Transfer using Real-Time Super Resolution Convolutional Neural Networks

2022 ◽  
Author(s):  
Ishaan Lodha ◽  
Lakshana Kolur ◽  
Keerthan Krishnan ◽  
Kumar Dheenadayalan ◽  
Dinkar Sitaram ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Super Resolution
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