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.

scholarly journals Application of Histogram Equalization for Image Enhancement in Corrosion Areas

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jianbin Xiong ◽  
Dezheng Yu ◽  
Qi Wang ◽  
Lei Shu ◽  
Jian Cen ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Histogram Equalization ◽  
Experimental Results ◽  
Blue Color ◽  
Low Contrast ◽  
Saturation Intensity ◽  
Enhancement Algorithm ◽  
Intensity Model

In this paper, an image enhancement algorithm is presented for identification of corrosion areas and dealing with low contrast present in shadow areas of an image. This algorithm uses histogram equalization processing under the hue-saturation-intensity model. First of all, an etched image is transformed from red-green-blue color space to hue-saturation-intensity color space, and only the luminance component is enhanced. Then, part of the enhanced image is combined with the original tone component, followed by saturation and conversion to red-green-blue color space to obtain the enhanced corrosion image. Experimental results show that the proposed method significantly improves overall brightness, increases contrast details in shadow areas, and strengthens identification of corrosion areas in the image.

scholarly journals Multi-Feature Guided Low-Light Image Enhancement

2021 ◽  
Vol 11 (11) ◽  
pp. 5055
Author(s):  
Hong Liang ◽  
Ankang Yu ◽  
Mingwen Shao ◽  
Yuru Tian
Keyword(s):  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Low Light ◽  
Low Contrast ◽  
Light Image ◽  
Affine Model ◽  
The Neural Network ◽  
Low Visibility ◽  
Model Robustness ◽  
Traditional Image

Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or even miss the detection target. However, recalibrating the dataset for this type of image will face problems such as increased cost or reduced model robustness. To solve this kind of problem, we propose a low-light image enhancement model based on deep learning. In this paper, the feature extraction is guided by the illumination map and noise map, and then the neural network is trained to predict the local affine model coefficients in the bilateral space. Through these methods, our network can effectively denoise and enhance images. We have conducted extensive experiments on the LOL datasets, and the results show that, compared with traditional image enhancement algorithms, the model is superior to traditional methods in image quality and speed.

scholarly journals Deep Refinement Network for Natural Low-Light Image Enhancement in Symmetric Pathways

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 491 ◽  
Author(s):  
Lincheng Jiang ◽  
Yumei Jing ◽  
Shengze Hu ◽  
Bin Ge ◽  
Weidong Xiao
Keyword(s):  
Image Enhancement ◽  
Resolution Enhancement ◽  
Global Features ◽  
Low Light ◽  
Low Contrast ◽  
Light Image ◽  
Fast Processing ◽  
Mixed Noise ◽  
The Cost ◽  
Camera Sensors

Due to the cost limitation of camera sensors, images captured in low-light environments often suffer from low contrast and multiple types of noise. A number of algorithms have been proposed to improve contrast and suppress noise in the input low-light images. In this paper, a deep refinement network, LL-RefineNet, is built to learn from the synthetical dark and noisy training images, and perform image enhancement for natural low-light images in symmetric—forward and backward—pathways. The proposed network utilizes all the useful information from the down-sampling path to produce the high-resolution enhancement result, where global features captured from deeper layers are gradually refined using local features generated by earlier convolutions. We further design the training loss for mixed noise reduction. The experimental results show that the proposed LL-RefineNet outperforms the comparative methods both qualitatively and quantitatively with fast processing speed on both synthetic and natural low-light image datasets.

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

Low-light-level image enhancement algorithm based on integrated networks

2020 ◽  
Author(s):  
Peng Wang ◽  
Jiao Wu ◽  
Haiyan Wang ◽  
Xiaoyan Li ◽  
Yongxia Yang
Keyword(s):  
Image Enhancement ◽  
Light Level ◽  
Low Light ◽  
Integrated Networks ◽  
Enhancement Algorithm

Research on the Image Enhancement Algorithm of Pointer Instrument under Inadequate Light

2014 ◽  
Vol 615 ◽  
pp. 248-254 ◽  
Author(s):  
Lu Zhang ◽  
Jin Lin Zhang ◽  
Ting Rui ◽  
Yue Wang ◽  
Yan Nan Wang
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Traditional Method ◽  
Histogram Equalization ◽  
Light Conditions ◽  
Low Light ◽  
Homomorphic Filtering ◽  
The Impact ◽  
Image Details ◽  
Enhancement Algorithm

For image processing, the recognition of pointer instrument’s reading by computer vision highly depends on brightness. An image enhancement algorithm based on homomorphic filtering and histogram equalization is proposed in order to reduce the impact of low-light conditions on images of pointer instrument. It combines the methods of spatial with frequency domain, which enhances the image contrast and highlights the image details as well. Compared with the traditional method, the experiments show that the proposed method can eliminate the effect of inadequate light and also perform well in enhancing the image quality.

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.

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