Model Parameter Learning for Real-Time High-Resolution Image Enhancement

2020 ◽  
Vol 27 ◽  
pp. 1844-1848
Author(s):  
Yuda Song ◽  
Yunfang Zhu ◽  
Xin Du
Author(s):  
Yue Yu ◽  
Maosen Huang ◽  
Kuanhong Cheng ◽  
Huixin Zhou ◽  
Zhe Zhang ◽  
...  

2021 ◽  
Vol 5 (2) ◽  
pp. 50-61
Author(s):  
Uroš Hudomalj ◽  
Christopher Mandla ◽  
Markus Plattner

This paper presents FPGA implementations of image filtering and image averaging – two widely applied image preprocessing algorithms. The implementations are targeted for real time processing of high frame rate and high resolution image streams. The developed implementations are evaluated in terms of resource usage, power consumption, and achievable frame rates. For the evaluation, Microsemi’s Smartfusion2 Advanced Development Kit is used. It includes a SmartFusion2 M2S150 SoC FPGA. The performance of the developed implementation of image filtering algorithm is compared to a solution provided by MATLAB’s Vision HDL Toolbox, which is evaluated on the same platform. The performance of the developed implementations are also compared with FPGA implementations found in existing publications, although those are evaluated on different FPGA platforms. Difficulties with performance comparison between implementations on different platforms are addressed and limitations of processing image streams with FPGA platforms discussed.


Sign in / Sign up

Export Citation Format

Share Document