A fuzzy enhancement method for infrared vehicle target image based on genetic algorithm

For images transfer, different embedding system exist which works by creating a mosaic image from the source image and recovery from the target image using some sort of algorithm. In current study, a method is proposed using the genetic algorithm for recovery of image from the source image. The algorithm utilized is genetic algorithm which is a search method along with another additional technique for obtaining higher robustness and security. The proposed methodology works by dividing the source image into smaller parts which are fitted into target image using the lossless compression. The mosaic image is recovered at retrieving side by the permutation array which is recovered and mapped using the pre-select key.

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Image Enhancement for Inspection of Cable Images Based on Retinex Theory and Fuzzy Enhancement Method in Wavelet Domain

Symmetry ◽

10.3390/sym10110570 ◽

2018 ◽

Vol 10 (11) ◽

pp. 570 ◽

Cited By ~ 3

Author(s):

Xuhui Ye ◽

Gongping Wu ◽

Le Huang ◽

Fei Fan ◽

Yongxiang Zhang

Keyword(s):

Image Quality ◽

Power Transmission ◽

Low Frequency ◽

Speckle Noise ◽

Standard Test ◽

Low Contrast ◽

Inspection Robot ◽

Fuzzy Enhancement ◽

Enhancement Method ◽

Frequency Components

Inspection images of power transmission line provide vision interaction for the operator and the environmental perception for the cable inspection robot (CIR). However, inspection images are always contaminated by severe outdoor working conditions such as uneven illumination, low contrast, and speckle noise. Therefore, this paper proposes a novel method based on Retinex and fuzzy enhancement to improve the image quality of the inspection images. A modified multi-scale Retinex (MSR) is proposed to compensate the uneven illumination by processing the low frequency components after wavelet decomposition. Besides, a fuzzy enhancement method is proposed to perfect the edge information and improve contrast by processing the high frequency components. A noise reduction procedure based on soft threshold is used to avoid the noise amplification. Experiments on the self-built standard test dataset show that the algorithm can improve the image quality by 3–4 times. Compared with several other methods, the experimental results demonstrate that the proposed method can obtain better enhancement performance with more homogeneous illumination and higher contrast. Further research will focus on improving the real-time performance and parameter adaptation of the algorithm.

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Application of genetic algorithm in target image processing of TV guided missile

MATEC Web of Conferences ◽

10.1051/matecconf/20164401021 ◽

2016 ◽

Vol 44 ◽

pp. 01021

Author(s):

Jing Feng He ◽

Ru Liang Xiao ◽

Ming Ji ◽

Guang Zhen Zhang

Keyword(s):

Genetic Algorithm ◽

Image Processing ◽

Target Image

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Research on Automatic Detection Technique for Pebrine Image Based on Computer Vision

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.159.383 ◽

2010 ◽

Vol 159 ◽

pp. 383-387 ◽

Cited By ~ 1

Author(s):

Xin Yu Hu ◽

Zuo Bing Chen ◽

Dao De Zhang ◽

Guang You Yang

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Image Segmentation ◽

Automatic Detection ◽

Shape Parameters ◽

Target Image ◽

Accurate Identification ◽

Region Labeling ◽

Enhancement Algorithm ◽

Light Green

Aiming to achieve the automatic detection and accurate identification of pebrine images, the fuzzy contrast enhancement algorithm was utilized to enhance the contrast of the target image in order to improve the image’s quality; Owing to the color character of light green for the pebrine, the image segmentation technique based on the HSI model can be applied to extract the pebeine image, at the same time, the morphology theory can be adopted to remove the noises such as the hole noise and point noise, and to separate the bond particles; The region labeling can be done on the binary image after the image segmentation, then the shape parameters of pebrine can be extracted, by making full use of the feature parameters, the method of neural network based on genetic algorithm is applied to recognize the pebrine image. These experiments show that the method has achieved satisfactory image recognition results.

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