A Parallel Retinex Image Enhancement Algorithm Based on OpenMP

2019 ◽  
pp. 376-381
Author(s):  
Shixiong Cheng ◽  
Bin Liu ◽  
Dongjian He ◽  
Jinrong He ◽  
Yuancheng Li ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Enhancement Algorithm

scholarly journals A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3583 ◽  
Author(s):  
Shiping Ma ◽  
Hongqiang Ma ◽  
Yuelei Xu ◽  
Shuai Li ◽  
Chao Lv ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Color Model ◽  
Low Light ◽  
Image Brightness ◽  
Low Contrast ◽  
Light Sensor ◽  
Hsi Color Model ◽  
Art Research ◽  
Enhancement Algorithm

Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the final improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast significantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.

Feature-Linking Model for Image Enhancement

2016 ◽  
Vol 28 (6) ◽  
pp. 1072-1100 ◽  
Author(s):  
Kun Zhan ◽  
Jicai Teng ◽  
Jinhui Shi ◽  
Qiaoqiao Li ◽  
Mingying Wang
Keyword(s):  
Neural Networks ◽  
Image Enhancement ◽  
Human Visual System ◽  
Input Image ◽  
Temporal Coding ◽  
Essential Dimension ◽  
Neural Representations ◽  
Gamma Band Oscillations ◽  
Processing Mechanisms ◽  
Enhancement Algorithm

Inspired by gamma-band oscillations and other neurobiological discoveries, neural networks research shifts the emphasis toward temporal coding, which uses explicit times at which spikes occur as an essential dimension in neural representations. We present a feature-linking model (FLM) that uses the timing of spikes to encode information. The first spiking time of FLM is applied to image enhancement, and the processing mechanisms are consistent with the human visual system. The enhancement algorithm achieves boosting the details while preserving the information of the input image. Experiments are conducted to demonstrate the effectiveness of the proposed method. Results show that the proposed method is effective.

scholarly journals Research on underwater image enhancement algorithm based on improved DCP

2021 ◽  
Vol 2083 (4) ◽  
pp. 042008
Author(s):  
Zhe Wu ◽  
Jianfgui Han ◽  
Chenghao Cao
Keyword(s):  
Image Enhancement ◽  
Transfer Rate ◽  
Color Correction ◽  
Dark Channel Prior ◽  
Underwater Image ◽  
Depth Analysis ◽  
Dark Channel ◽  
Color Bias ◽  
Enhancement Algorithm ◽  
Priority Algorithm

Abstract All for underwater images, there are some drawbacks, such as low definition, serious color bias, dark brightness, etc. On the basis of in-depth analysis of common image enhancement algorithms, This paper uses the improved dark channel priority algorithm to enhance the underwater image, Improving the contrast of underwater images and color correction of underwater images. Color correction is added based on dark channel prior algorithm; Make the image look more even, higher contrast, more acceptable. The improved algorithm model has a higher transfer rate; PSNR is more balanced and has better contrast to meet the requirements of underwater image observation.

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