Computationally efficient contrast enhancement algorithm suitable for real-time digital hardware

2004 ◽  
Author(s):  
Douglas R. Droege ◽  
John J. Forsthoefel ◽  
Russell C. Hardie
Keyword(s):  
Contrast Enhancement ◽  
Real Time ◽  
Computationally Efficient ◽  
Digital Hardware ◽  
Enhancement Algorithm

A Hardware-Oriented Contrast Enhancement Algorithm for Real-Time Applications

2018 ◽  
Author(s):  
Chia-En Chang ◽  
Shih-Lun Chen ◽  
Chiung-An Chen ◽  
Ting-Lan Lin ◽  
Yao-Tsung Kuo ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Real Time ◽  
Real Time Applications ◽  
Enhancement Algorithm

Real-Time Advanced Contrast Enhancement Algorithm

2003 ◽  
pp. 691-698
Author(s):  
Tae-Chan Kim ◽  
Chang-Won Huh ◽  
Meejoung Kim ◽  
Bong-Young Chung ◽  
Soo-Won Kim
Keyword(s):  
Contrast Enhancement ◽  
Real Time ◽  
Enhancement Algorithm

An Improved Adaptive Unsharp Masking Image Enhancement Algorithm

2013 ◽  
Vol 325-326 ◽  
pp. 1547-1550
Author(s):  
Li Zhu ◽  
Chun Qiang Zhu
Keyword(s):  
Contrast Enhancement ◽  
Real Time ◽  
Image Enhancement ◽  
Digital Tv ◽  
Image Sharpening ◽  
Real Time Processing ◽  
Time Processing ◽  
Unsharp Masking ◽  
Image Contrast Enhancement ◽  
Enhancement Algorithm

When needing a reliable adaptive image contrast enhancement in real-time processing such as digital TV postprocessing,This goal is achieved by an improved adaptive unsharp masking image enhancement algorithm in the paper. The proposed improved adaptive unsharp masking filter controls the contribution of the sharpening path by the output of Laplacian and works in such a way that contrast enhancement occurs in high detail areas and little or no image sharpening occurs in smooth areas. The experiment shows that this improved algorithm greatly reduce the complexity of computing and can be reliably used.

A real-time contrast enhancement algorithm for infrared images based on plateau histogram

2006 ◽  
Vol 48 (1) ◽  
pp. 77-82 ◽  
Author(s):  
Bing-jian Wang ◽  
Shang-qian Liu ◽  
Qing Li ◽  
Hui-xin Zhou
Keyword(s):  
Contrast Enhancement ◽  
Real Time ◽  
Infrared Images ◽  
Enhancement Algorithm

RSNet: Rail semantic segmentation network for extracting aerial railroad images

2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Obstacle Detection ◽  
Aerial Images ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

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