An Image Fuzzy Enhancement Adopting Linear Membership Function

2014 ◽  
Vol 543-547 ◽  
pp. 2213-2216
Author(s):  
Dong Ming Zhang ◽  
Li Jia Chen ◽  
Wei Gao
Keyword(s):  
Membership Function ◽  
Low Contrast ◽  
Fuzzy Enhancement ◽  
Linear Scheme ◽  
Degraded Image ◽  
Level Information ◽  
Image Edges ◽  
Contrast Stretching ◽  
Fuzzy Image ◽  
Stretching Effect

S.K.Pal's fuzzy set theory has been used to deal with the degraded image, wherein the image edges are uncertain and inaccurate. But Pals algorithm losses low grey level information of the original image and the grayscales of the image can not be extended. A fuzzy image enhancement based on linear membership function is proposed through the analysis of classical Pal's fuzzy enhancement. This algorithm avoids the lost of low grades information of the image as well as increases the image's whole grey scales. It is very suited for low grades, low contrast images such as X-ray images. As a result of the linear transformation comparing with Pal's non-linear scheme, the image processing speed is also improved. Experiment results show that the proposed method outperforms traditional Pal's in terms of contrast stretching effect and speed.

Image Defogging Based on Fog Veil Subtraction

2011 ◽  
Vol 121-126 ◽  
pp. 887-891
Author(s):  
Bin Xie ◽  
Fan Guo ◽  
Zi Xing Cai
Keyword(s):  
Original Image ◽  
Single Image ◽  
Image Defogging ◽  
Real Scene ◽  
Degraded Image ◽  
The Veil ◽  
Contrast Stretching ◽  
Reflectance Map ◽  
Fog Density

In this paper, we propose a new defog algorithm based on fog veil subtraction to remove fog from a single image. The proposed algorithm first estimates the illumination component of the image by applying smoothing to the degraded image, and then obtains the uniform distributed fog veil through a mean calculation of the illumination component. Next, we multiply the uniform veil by the original image to obtain a depth-like map and extract its intensity component to produce a fog veil whose distribution is according with real fog density of the scene. Once the fog veil is calculated, the reflectance map can be obtained by subtracting the veil from the degraded image. Finally, we apply an adaptive contrast stretching to the reflectance map to obtain an enhanced result. This algorithm can be easily extended to video domains and is verified by both real-scene photographs and videos.

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

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 570 ◽  
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.

Neutron Image Restoration by Combining Richardson-Lucy With Steering Kernel

2013 ◽  
Author(s):  
Shuang Qiao ◽  
Qiao Wang ◽  
Jipeng Huang
Keyword(s):  
Imaging System ◽  
Signal To Noise Ratio ◽  
Neutron Imaging ◽  
Low Contrast ◽  
Image Characteristics ◽  
Restoration Algorithm ◽  
Degraded Image ◽  
Neutron Image ◽  
Noise Amplification ◽  
Work Done

Neutron image obtained from a small digital neutron imaging system, always has characteristics of low contrast, blurred edges and serious noise. It is disadvantageous to further analyse information about the sample’s internal structure, so it is essential for the observer to process the degraded image to improve its visual quality. In order to avoid the noise amplification problem of the original Richardson-Lucy (R-L) algorithm, which is adopted to recover degraded image, a restoration algorithm by combining R-L algorithm with Steering Kernel (S-K) algorithm for neutron image is presented in this paper. First S-K algorithm is applied to restrain the noise of the blurred noisy neutron image, as well as improving the signal-to-noise ratio of the image, and then R-L algorithm is used to reconstruct the blurred noisy image. The proposed algorithm is able to make up for the deficiency of R-L algorithm in dealing with the noise amplification problem, which is caused by the repeated iteration, while retaining the details of the image characteristics as much as possible. Comparative experimental results show that the algorithm can obtain satisfactory restoration visual effect for neutron image. The details of the work done are described in this paper.

scholarly journals Image mosaicing by using random seeds generation based on fuzzy membership function

2018 ◽  
Vol 7 (2.2) ◽  
pp. 70
Author(s):  
Darius Shyafary ◽  
Rony H ◽  
Rheo Malani ◽  
Anggri Sartika W
Keyword(s):  
Image Processing ◽  
Information Processing ◽  
Computer Graphics ◽  
Membership Function ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Image Mosaic ◽  
Good Image ◽  
Input And Output ◽  
Fuzzy Image

A mosaic is a combination of two or more images with various combining techniques. One of the computer graphics applications is the image mosaic used for various purposes such as texture maps and better image backgrounds. One of the important things in making image mosaic is how to create small pieces of the image in such a way that it produces a good image mosaic. A number of methods have been proposed to build an image mosaic system that produces good mosaic results, but it usually requires complicated calculations. Fuzzy image processing is a form of information processing that input and output both images. This is a collection of fuzzy approaches that understand, represent and process their images, segments, and features as a fuzzy set. In this study, fuzzy image processing concept is used to create image mosaic by random seed generation using Fuzzy Membership Function (MF).  

Edge Enhancement from Low-Light Image by Convolutional Neural Network and Sigmoid Function

IJOSTHE ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 8
Author(s):  
Puspad Kumar Sharma ◽  
Nitesh Gupta ◽  
Anurag Shrivastava
Keyword(s):  
Input Image ◽  
Sigmoid Function ◽  
Single Image ◽  
Low Light ◽  
Lighting Condition ◽  
Low Contrast ◽  
General Terms ◽  
Degraded Image ◽  
Result Analysis

Due to camera resolution or any lighting condition, captured image are generally over-exposed or under-exposed conditions. So, there is need of some enhancement techniques that improvise these artifacts from recorded pictures or images. So, the objective of image enhancement and adjustment techniques is to improve the quality and characteristics of an image. In general terms, the enhancement of image distorts the original numerical values of an image. Therefore, it is required to design such enhancement technique that do not compromise with the quality of the image. The optimization of the image extracts the characteristics of the image instead of restoring the degraded image. The improvement of the image involves the degraded image processing and the improvement of its visual aspect. A lot of research has been done to improve the image. Many research works have been done in this field. One among them is deep learning. Most of the existing contrast enhancement methods, adjust the tone curve to correct the contrast of an input image but doesn’t work efficiently due to limited amount of information contained in a single image. In this research, the CNN with edge adjustment is proposed. By applying CNN with Edge adjustment technique, the input low contrast images are capable to adapt according to high quality enhancement. The result analysis shows that the developed technique significantly advantages over existing methods.

A Novel Image Contrast Restoration Algorithm for Fog

2013 ◽  
Vol 709 ◽  
pp. 534-537
Author(s):  
Hui Xian Lv ◽  
Zhi Gang Zhao ◽  
Yan Feng Xu
Keyword(s):  
Image Restoration ◽  
Histogram Equalization ◽  
Low Contrast ◽  
Filtering Method ◽  
Restoration Algorithm ◽  
Homomorphic Filtering ◽  
Degraded Image ◽  
Soft Threshold ◽  
Degraded Images ◽  
Novel Algorithm

Images captured in fog suffer from low contrast, restoration of fog- degraded images are needed. In this paper, a novel algorithm of image restoration based on wavelet semi-soft threshold is presented. The results show detail restoration and de-noising are improved effectively comparing with Histogram equalization and homomorphic filtering method. It can be concluded that the new algorithm enhanced the contrast of fog-degraded image well.

scholarly journals Image Defogging Algorithm Based on Sparse Representation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Di Fan ◽  
Xinyun Guo ◽  
Xiao Lu ◽  
Xiaoxin Liu ◽  
Bo Sun
Keyword(s):  
Sparse Representation ◽  
Color Space ◽  
Image Brightness ◽  
Low Contrast ◽  
Image Defogging ◽  
Degraded Image ◽  
Hsi Space ◽  
Definition Of ◽  
Nonlinear Stretching ◽  
Sparse Features

Aiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging algorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level wavelet transform extract features of image brightness components. Then, it uses the K-SVD algorithm training dictionary and learns the sparse features of the fog-free image to reconstructed I-components of the fog image. Using the nonlinear stretching approach for saturation component improves the brightness of the image. Finally, convert from HSI space to RGB color space to get the defog image. Experimental results show that the algorithm can effectively improve the contrast and visual effect of the image. Compared with several common defog algorithms, the percentage of image saturation pixels is better than the comparison algorithm.

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