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

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.

2020 ◽  
Vol 57 (16) ◽  
pp. 161019
Author(s):  
林昌 Lin Chang ◽  
周海峰 Zhou Haifeng ◽  
陈武 Chen Wu

2014 ◽  
Vol 513-517 ◽  
pp. 3362-3367
Author(s):  
Yin Gao ◽  
Li Jun Yun ◽  
Jun Sheng Shi ◽  
Fei Yan Cheng

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.


2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jianbin Xiong ◽  
Dezheng Yu ◽  
Qi Wang ◽  
Lei Shu ◽  
Jian Cen ◽  
...  

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.


2014 ◽  
Vol 989-994 ◽  
pp. 3838-3843
Author(s):  
Yin Gao ◽  
Li Jun Yun ◽  
Jun Sheng Shi ◽  
Fei Yan Cheng

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.


Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3583 ◽  
Author(s):  
Shiping Ma ◽  
Hongqiang Ma ◽  
Yuelei Xu ◽  
Shuai Li ◽  
Chao Lv ◽  
...  

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.


2014 ◽  
Vol 989-994 ◽  
pp. 3798-3801
Author(s):  
Zhi Gang Zhang ◽  
Shi Qiang Yan ◽  
Peng Geng

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.


Author(s):  
Akira Taguchi

There are many color systems. Some systems are correspond to the human visual system, such as the Munsell color system. Other systems are formulated to ease data processing in machines, such as RGB color space. At first, Munsell color system is introduced in this paper. Next, RGB color system and hue-saturation-intensity (HSI) color system which is derived from RGB color systems are reviewed. HSI color system is important, because HSI color system is closely related to Munsell color system. We introduce the advantage and drawbacks of the conventional HSI color space. Furthermore, the improved HSI color system is introduced. The second half of this paper, we introduce a lot of color image enhancement methods based on the histogram equalization or the differential histogram equalization. Since hue preserving is necessary for color image processing, intensity processing methods by using both intensity and saturation in HSI color space are reviewed. Finally, hue preserving color image enhancement methods in RGB color system are explained.


Symmetry ◽  
2017 ◽  
Vol 9 (6) ◽  
pp. 93 ◽  
Author(s):  
Lei Si ◽  
Zhongbin Wang ◽  
Rongxin Xu ◽  
Chao Tan ◽  
Xinhua Liu, ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document