Paper Info
Title
FPGA-Based Low-Visibility Enhancement Accelerator for Video Sequence by Adaptive Histogram Equalization With Dynamic Clip-Threshold
Abstract
In the natural and practical scenario, the captured video sequence under bad weather situations or low light conditions often suffers from poor visibility and low-contrast problems. This hurts the performance of the high-level processing, e.g. object tracking or recognition. In this paper, we develop an FPGA-based low-visibility enhancement accelerator for video sequence by adaptive histogram equalization with dynamic clip-threshold (AHEwDC) which is determined by the visibility assessment. The main goal is to improve the low visibility with high image quality for both hazy and low-light video sequences in real-time. Firstly, a concept to quantify the visual perception based on supervised learning is to estimate the visibility score. Then, to avoid the problem of noise amplification in the conventional method, we propose a visibility assessment model to find an optimal clip-threshold. The contrast energy of gray channel, yellow-blue channel and red-green channel, average saturation, and gradients are statistical features in the model to describe the visibility of an image. Finally, to meet the speed requirement for video sequence processing, a specified hardware architecture for both visibility assessment and AHEwDC is implemented on FPGA. Besides, a mean spatial filter for cumulative distribution functions (CDFs) of the AHE is developed for suppressing the noise caused by a single-color local region. The demonstration system on the DE1-SoC platform with the Intel Cyclone V FPGA device with the max working frequency of 75.84 MHz is capable of processing 30 fps FHD (1920 × 1080) video.
Year
DOI
Venue
2020
10.1109/TCSI.2020.3010634
IEEE Transactions on Circuits and Systems I: Regular Papers
Keywords
DocType
Volume
Low visibility enhancement,histogram equalization,visual perception,image enhancement,FPGA design
Journal
67
Issue
ISSN
Citations 
11
1549-8328
2
PageRank 
References 
Authors
0.37
0
6
Name
Order
Citations
PageRank
Canran Xu120.37
Zizhao Peng220.37
Xuanzhen Hu320.37
Zhang Wei439253.03
Lei Chen5177.06
Fengwei An6289.61