Image local fuzzy enhancement method based on Improved Wavelet Transform

Author(s):  
Qing He ◽  
Feng Pan
2012 ◽  
Vol 198-199 ◽  
pp. 238-243 ◽  
Author(s):  
Wen Sheng Guo ◽  
Feng Chen ◽  
Zhao You Sun ◽  
Xi Jun Wang

The traditional image magnify method usually have some defects on details. This paper gives a new infrared image magnification and enhancement method which is based on wavelet reconstruction and gradation segment. In this method, first of all, make wavelet transform on the image, get the high-frequency coefficient. Apply the Newton differential algorithm enhance the high-frequency coefficient as the high-frequency part of the magnified image, treat the original image as the low-frequency part , make the wavelet reconstruction ,then get the magnified image. To enhance the magnified image, according to the double gray threshold, segment the image into high gray segment corresponding to target, low gray segment corresponding to background, and middle gray segment corresponding to transition sector. Then, make linear extension to them respectively; the result is the magnified image. Experiments indicate, this method is effective on distinguishing high-energy target from low-energy target (the low-energy target is the primary one) and displaying the details of image(edge profile of the bomb).


Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 570 ◽  
Author(s):  
Xuhui Ye ◽  
Gongping Wu ◽  
Le Huang ◽  
Fei Fan ◽  
Yongxiang Zhang

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.


2019 ◽  
Author(s):  
Xiang-Yu Jia ◽  
Chang-Lei DongYe

Abstract. The seismic section image contains a wealth of texture detail information, which is important for the interpretation of the formation profile information. In order to enhance the texture detail of the image while keeping the structural information of the image intact, a multi-scale enhancement method based on wavelet transform is proposed. First, the image is wavelet decomposed to obtain a low frequency structural component and a series of high frequency texture detail components; Secondly, bilateral texture filtering is performed on the low-frequency structural components to filter out high-frequency noise while maintaining the edges of the image; adaptive enhancement is performed on the high-frequency detail components to filter out low-frequency noise while enhancing detail; Finally, the processed high and low frequency components are reconstructed by wavelet can obtained the seismic section image with enhanced detail. The method of this paper enhances the texture detail information in the image while preserving the edge of the image.


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