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.

Low-light image enhancement algorithm based on an atmospheric physical model

2020 ◽  
Vol 79 (43-44) ◽  
pp. 32973-32997
Author(s):  
Xiaomei Feng ◽  
Jinjiang Li ◽  
Zhen Hua
Keyword(s):  
Image Enhancement ◽  
Physical Model ◽  
Low Light ◽  
Light Image ◽  
Enhancement Algorithm

scholarly journals Retinex-Based Fast Algorithm for Low-Light Image Enhancement

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 746
Author(s):  
Shouxin Liu ◽  
Wei Long ◽  
Lei He ◽  
Yanyan Li ◽  
Wei Ding
Keyword(s):  
Image Enhancement ◽  
Fast Algorithm ◽  
Dynamic Range ◽  
Color Space ◽  
Mapping Function ◽  
Blue Color ◽  
Gray Level ◽  
Low Light ◽  
Light Image ◽  
Adaptive Gamma Correction

We proposed the Retinex-based fast algorithm (RBFA) to achieve low-light image enhancement in this paper, which can restore information that is covered by low illuminance. The proposed algorithm consists of the following parts. Firstly, we convert the low-light image from the RGB (red, green, blue) color space to the HSV (hue, saturation, value) color space and use the linear function to stretch the original gray level dynamic range of the V component. Then, we estimate the illumination image via adaptive gamma correction and use the Retinex model to achieve the brightness enhancement. After that, we further stretch the gray level dynamic range to avoid low image contrast. Finally, we design another mapping function to achieve color saturation correction and convert the enhanced image from the HSV color space to the RGB color space after which we can obtain the clear image. The experimental results show that the enhanced images with the proposed method have better qualitative and quantitative evaluations and lower computational complexity than other state-of-the-art methods.

Enhancement Algorithm of Color Fog Image Based on the Adaptive Scale and Multi-Parameter Correction

2014 ◽  
Vol 989-994 ◽  
pp. 3838-3843
Author(s):  
Yin Gao ◽  
Li Jun Yun ◽  
Jun Sheng Shi ◽  
Fei Yan Cheng
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Weighted Average ◽  
Objective Evaluation ◽  
Gaussian Kernel ◽  
Multi Scale ◽  
Hsi Color Space ◽  
Reflection Component ◽  
Parameter Correction ◽  
Enhancement Algorithm

To deal with the image contrast and color fidelity details problem in the traditional Center around the Retinex image enhancement algorithms, Enhancement Algorithm of Color Fog Image Based on the Adaptive Scale and Multi-parameter correction is proposed. First, the saturation component of the HSI color space is applied to the nonlinear correction. Then according to the brightness of the image pixel values, it can calculates the scale of the Gaussian kernel, and then proceeds to do the multi-parameter correction for the estimates of the reflection component, obtains the multi-scale image by the weighted average. At last, the obtained image is used to adjust the display correction, image sharpening and smoothing, and being superimposed reflection components, achieving the image enhancement. Through the subjective observation and objective evaluation, this algorithm is better than the traditional center around Retinex algorithm in treatment effect, and also saves a large amount of processing time.

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