Enhancement Algorithm of Color Fog Image Based on the Adaptive Scale and S-Cosine Curve

2014 ◽  
Vol 513-517 ◽  
pp. 3362-3367
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 ◽  
Correction Function ◽  
Multi Scale ◽  
Cosine Curve ◽  
Reflection Component ◽  
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 s-cosine curve is proposed. Firstly, the image is transformed into the RGB color space. Then the each channel pixel values can be stretched the grayscale range by S-cosine curve and introduces the local correction function. It can calculate the scale of the Gaussian kernel, and then proceeds to do the Gamma correction for the estimates of the reflection component, obtains the multi-scale image by the weighted average. Afterwards, the obtained image is used to global nonlinear correction, image sharpening and smoothing, and being superimposed reflection components, achieving the image enhancement. At last, it can carry on the intensity adjustment and grayscale adjustment for the obtained image. Through the subjective observation and objective evaluation, this algorithm is better than the traditional center around Retinex algorithm and MSRCR algorithm in processing effect.

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.

scholarly journals Improved Bilateral Filtering for a Gaussian Pyramid Structure-Based Image Enhancement Algorithm

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 258 ◽  
Author(s):  
Chang Lin ◽  
Hai-feng Zhou ◽  
Wu Chen
Keyword(s):  
Image Enhancement ◽  
Gaussian Function ◽  
Color Space ◽  
Human Observer ◽  
Bilateral Filtering ◽  
Saturation Intensity ◽  
Gaussian Pyramid ◽  
Retinex Algorithm ◽  
Reflection Component ◽  
Enhancement Algorithm

To address the problem of unclear images affected by occlusion from fog, we propose an improved Retinex image enhancement algorithm based on the Gaussian pyramid transformation. Our algorithm features bilateral filtering as a replacement for the Gaussian function used in the original Retinex algorithm. Operation of the technique is as follows. To begin, we deduced the mathematical model for an improved bilateral filtering function based on the spatial domain kernel function and the pixel difference parameter. The input RGB image was subsequently converted into the Hue Saturation Intensity (HSI) color space, where the reflection component of the intensity channel was extracted to obtain an image whose edges were retained and are not affected by changes in brightness. Following reconversion to the RGB color space, color images of this reflection component were obtained at different resolutions using Gaussian pyramid down-sampling. Each of these images was then processed using the improved Retinex algorithm to improve the contrast of the final image, which was reconstructed using the Laplace algorithm. Results from experiments show that the proposed algorithm can enhance image contrast effectively, and the color of the processed image is in line with what would be perceived by a human observer.

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.

Wavelet Transform Adjustment Method Study in Color Image Enhancement

2014 ◽  
Vol 989-994 ◽  
pp. 3798-3801
Author(s):  
Zhi Gang Zhang ◽  
Shi Qiang Yan ◽  
Peng Geng
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
Basic Principle ◽  
Color Image ◽  
Experimental Result ◽  
Adjustment Method ◽  
Color Image Enhancement ◽  
Reflection Component ◽  
Enhancement Algorithm

In order to improve the ensemble of color image, this paper proposes homomorphism decomposition—wavelet enhancement algorithm based on the basic principle of Wavelet Transform. We separate the incidence component and reflection component of the image by homomorphism decomposition, and then combine wavelet transform to enhance image as well as reserve details. The experimental result shows that the adaption and effect is obviously superior to MSRCR.

Improved multi-scale retinex image enhancement algorithm

2010 ◽  
Vol 30 (8) ◽  
pp. 2091-2093 ◽  
Author(s):  
Xiao-ming WANG ◽  
Chang HUANG ◽  
Quan-bin LI ◽  
Jin-gao LIU
Keyword(s):  
Image Enhancement ◽  
Multi Scale ◽  
Enhancement Algorithm

An improved infrared image enhancement algorithm based on multi-scale decomposition

2014 ◽  
Author(s):  
Honghui Zhang ◽  
Haibo Luo ◽  
Xin-rong Yu ◽  
Qing-hai Ding
Keyword(s):  
Image Enhancement ◽  
Infrared Image ◽  
Multi Scale ◽  
Enhancement Algorithm

Improved Multi Scale DR Medical Image Enhancement Algorithm

2014 ◽  
Vol 530-531 ◽  
pp. 413-417
Author(s):  
Xiao Jing Sun ◽  
Ai Bin Chen
Keyword(s):  
Image Enhancement ◽  
Low Frequency ◽  
Frequency Component ◽  
Image Feature ◽  
High Frequency Component ◽  
Image Processing Algorithm ◽  
Multi Scale ◽  
Gaussian Pyramid ◽  
Layer Image ◽  
Enhancement Algorithm

The original DR image is decomposed into different scale and frequency of the band image sequence by using Laplace gaussian pyramid model methods. Using multi-scale image enhancement algorithm to enhance the High frequency component of the decomposed image, Then adjust the light of the low frequency part to make the reconstructed image illumination contrast more reasonable. The enhanced process according to different frequency layer image feature make the different gain weight for the different frequency layer image characteristics,so different frequency image layer realize respectively noise smoothing, dimensionality reduction and enhance the effect of edge character.The simulation experiments showed that this Image Processing Algorithm effect is very good.

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