Skin Detection Model Research Based on Image Enhancement

2011 ◽  
Vol 65 ◽  
pp. 260-263
Author(s):  
Guo Liang Yang ◽  
Zhi Lin Cheng ◽  
Li Zhang
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Original Image ◽  
Skin Model ◽  
Detection Model ◽  
Model Research ◽  
Linear Transform ◽  
Double Skin ◽  
Enhancement Algorithm ◽  
Better Than

Histogram element gradation distribution of the original image concentrates in the low gradation level, and after histogram equalization processed the image is bright and unconspicuous in details. In order to improve the situation, this paper presents an improved image enhancement algorithm .In the algorithm the original image is transformed by the conventional histogram equalization and mapped the histogram equalization processing image as far as possible within the scope of mapping. Then linear transform is used to enhance contrast and apply to mix skin complexion model to extract. Experiments prove that this method is better than double skin model detection at testing results, especially in the eyes and mouth.

An Improved Color Image Enhancement Algorithm Based on 3-D Color Histogram Equalization

2013 ◽  
Vol 321-324 ◽  
pp. 1133-1137
Author(s):  
Yu Ting Song ◽  
Xiu Hua Ji ◽  
Shi Lin Zhao
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Histogram Equalization ◽  
Color Images ◽  
Color Histogram ◽  
Test Results ◽  
Gray Scale ◽  
Color Image Enhancement ◽  
Equalization Algorithm ◽  
Enhancement Algorithm

This paper proposes an improved color image enhancement algorithm based on 3-D color histogram equalization algorithm. When the existed 3-D color histogram equalization algorithms in the literatures are applied in processing dim color images, the processed color images often turn pale due to the decrease of color-saturations and have false contours due to gray-scale merging phenomenon in the histogram equalization algorithm. In this paper, the proposed algorithm can make more pixels of the processed color images keep their color-saturations and reduce the gray-scale merging with Logarithmic histogram equalization algorithm. Test results with dim color images present a better effect of image enhancement.

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 A Novel Technique for Automatic Image Enhancement using HTHET Approach

2019 ◽  
Vol 8 (1) ◽  
pp. 26-31
Author(s):  
V. Murali ◽  
T. Venkateswarlu
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Information Content ◽  
Optimization Method ◽  
Histogram Equalization ◽  
Original Image ◽  
Enhancement Technique ◽  
Novel Technique ◽  
Equalization Technique ◽  
Histogram Transformation

Image enhancement techniques are methods used for producing images with better quality than the original image. None of the existing methods increase the information content of the image, and are usually of little interest for subsequent automatic analysis of images. In this paper, automated Image Enhancement is achieved by carrying out Histogram techniques. Histogram equalization (HE) is a spatial domain image enhancement technique, which effectively enhances the contrast of an image. We make use of Transformation and Hyperbolization techniques for automatic image enhancement. However, while it takes care of contrast enhancement, a modified histogram equalization technique, Histogram Transformation and Hyperbolization Equalization Technique (HTHET) using optimization method is proposed using EQHIST and LINHIST.

scholarly journals Classical and Fuzzy Based Image Enhancement Techniques for Banana Root Disease Diagnosis: A Review and Validation

2020 ◽  
Vol 13 (1) ◽  
pp. 50-62
Author(s):  
D. Suryaprabha ◽  
J. Satheeshkumar ◽  
N. Seenivasan
Keyword(s):  
Image Quality ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Plant Root ◽  
Histogram Equalization ◽  
Disease Diagnosis ◽  
Root Disease ◽  
Classical Point ◽  
Enhancement Algorithm

A vital step in automation of plant root disease diagnosis is to extract root region from the input images in an automatic and consistent manner. However, performance of segmentation algorithm over root images directly depends on the quality of input images. During acquisition, the captured root images are distorted by numerous external factors like lighting conditions, dust and so on. Hence it is essential to incorporate an image enhancement algorithm as a pre-processing step in the plant root disease diagnosis module. Image quality can be improved either by manipulating the pixels through spatial or frequency domain. In spatial domain, images are directly manipulated using their pixel values and alternatively in frequency domain, images are indirectly manipulated using transformations. Spatial based enhancement methods are considered as favourable approach for real time root images as it is simple and easy to understand with low computational complexity. In this study, real time banana root images were enhanced by attempting with different spatial based image enhancement techniques. Different classical point processing methods (contrast stretching, logarithmic transformation, power law transformation, histogram equalization, adaptive histogram equalization and histogram matching) and fuzzy based enhancement methods using fuzzy intensification operator and fuzzy if-then rule based methods were tried to enhance the banana root images. Quality of the enhanced root images obtained through different classical point processing and fuzzy based methods were measured using no-reference image quality metrics, entropy and blind image quality index. Hence, this study concludes that fuzzy based method could be deployed as a suitable image enhancement algorithm while devising the image processing modules for banana root disease diagnosis.

Perbandingan Performa Histogram Equalization untuk Peningkatan Kualitas Gambar Minim Cahaya pada Android

2020 ◽  
Vol 12 (2) ◽  
pp. 80-88
Author(s):  
Claudia Kenyta ◽  
Daniel Martomanggolo Wonohadidjojo
Keyword(s):  
Image Enhancement ◽  
Light Condition ◽  
Histogram Equalization ◽  
Low Light ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Better Than

When the photos are taken in low light condition, the quality of the results will not meet their expectation. Image Enhancement method can be used to enhance the quality of the photos taken in low light condition. One of the algorithms used is called Histogram Equalization (HE), that works using Histogram basis. The superiority of HE algorithm in enhancing the quality of the photos taken in low light condition is the simplicity of the algorithm itself and it does not need a high specification device for the algorithm to run. One variant of HE algorithm is Contrast Limited Adaptive Histogram Equalization (CLAHE). This paper shows the implementation of HE algorithm and its performance in enhancing the quality of photos taken in low light condition on Android based application and the comparison with CLAHE algorithm. The results show that, HE algorithm is better than CLAHE algorithm.

An Embedded Infrared Image Enhancement System Based on DSP and FPGA

2012 ◽  
Vol 505 ◽  
pp. 263-266
Author(s):  
Dong Mei Liu ◽  
Tao Zhang ◽  
Chuan Li Yin ◽  
Xiao Qiang Ji
Keyword(s):  
Real Time ◽  
Image Enhancement ◽  
High Speed ◽  
High Performance ◽  
Infrared Image ◽  
Processing System ◽  
Histogram Equalization ◽  
Running Time ◽  
Equalization Algorithm ◽  
Enhancement Algorithm

According to the disadvantage of the large noises of histogram equalization algorithm, a new adaptive image enhancement algorithm is presented. First, the statistical histogram of the infrared image is done. Then the threshold of plateaus Equalization is calculated and the statistical histogram is modified. Finally the bright values of the pixels of the image are changed. An embedded high speed image enhancement processing system on high performance DSP TMS320DM642 and FPGA was designed. Experimental results with real images shown that the system can improve the contrast of the infrared image, limit the noises of the enhancement image, and effectively enhance the infrared image, the running time of the program is shorter, so it can meet the requirements of real-time in the project.

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.

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