A robust approach to image enhancement based on fuzzy logic

Choi, Young Sik; Krishnapuram, Raghu

doi:10.1109/83.585232

Cited by 177 publications

(6 citation statements)

References 40 publications

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“…Since fuzzy set theory can handle the ambiguity problem of the image well [8] [9], it is introduced to image contrast enhancement, combined with the improved 2D-OTSU, which can adaptively enhance the contrast of image. The steps of new contrast enhancement algorithm are as follows:…”

Section: Image Contrast Enhancement Algorithmmentioning

confidence: 99%

Study on the Preprocessing Algorithm of Transmission Lines Video Monitoring Image

Sun¹,

Yang²,

Fan³

2010

2010 2nd International Conference on Information Engineering and Computer Science

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A two-dimension OTSU based on Simulated Annealing and Particle Swarm Optimization Algorithm is proposed for image segment and a novel contrast enhancement algorithm is presented based on it, and total variation denoising model is applied to transmission lines image denoising, aiming to the problems that the transmission lines image obtained in bad weather conditions by video monitoring system are low-contrast and often have noise. Experimental results show that the proposed preprocessing methods can solve these problems well, and the Simulated annealing particle swarm Optimization Algorithm has a better convergence and computing speed in the search of global solution. After preprocessing, image quality and segmentation effect are greatly improved, which can better meet the requirements of monitoring system for the identification of the image.

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Section: Image Contrast Enhancement Algorithmmentioning

confidence: 99%

Study on the Preprocessing Algorithm of Transmission Lines Video Monitoring Image

Sun¹,

Yang²,

Fan³

2010

2010 2nd International Conference on Information Engineering and Computer Science

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show abstract

“…In some situations it is possible to resort to some local and adaptive smoothing filters based on image statistics. Recently, fuzzy rulebased noise filtering algorithms have been demonstrated to work well as local and adaptive filters [32,33]. Let Fe be a linear filter that can be used to remove additive Gaussian noise.…”

Section: Preprocessing With Noise Removal and Edge Preservationmentioning

confidence: 99%

Fuzzy Based Image Segmentation

Basir

Zhu

Karray

2003

Fuzzy Filters for Image Processing

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Summary. In this chapter, we introduce some recently developed fuzzy based techniques for image segmentation. They are fuzzy thresholding, fuzzy rule-based inferencing scheme, fuzzy c-mean clustering, and fuzzy integral-based decision making. A fuzzy integral based region merging algorithm for image segmentation, which combines both region and edge features of the image, is then used to merge regions recursively according to the criterion of the maximum fuzzy integral. The region merging process is regarded as a nonlinear process that fuses objective evidence, in the form of a fuzzy membership function that reflects the similarities between adjacent regions with respect to each feature, with the apriori system's expectation of the importance of that evidence provided by the corresponding feature. Using the maximum fuzzy integral criterion, the target number of regions can be reached by recursively merging regions. To handle the parameter initiation problem faced in the computation of fuzzy integral, an algorithm for automatically choosing such parameters is formulated as an optimization problem. A simulated annealing algorithm is designed to explore the value space offuzzy densities and search the (near) optimal solution corresponding to a minimum cost value. This way, fuzzy densities are determined adaptively depending on the image at hand without requiring any human intervention. To evaluate the performance of the proposed approach, it is applied to magnetic resonance images (MRI) and natural images. The experimental results have demonstrated that the proposed approach brings out robust segmentation performance and outperforms other approaches efficiently.

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“…One of the best groups of the methods introduced for image enhancement and edge detection is based on the fuzzy logic theory 11 , 28 , 31 – 35 Fuzzy enhancement and edge detection on satellite remote sensing images were first introduced in Refs. 36 and 37, respectively 36 , 37 .…”

Section: Introductionmentioning

confidence: 99%

Development of the iterative edge detection method applied on blurred satellite images: state of the art

Rahimzadegan

Sadeghi

2016

J. Appl. Remote Sens

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.This paper aims to implement an iterative fuzzy edge detection (IFED) method on blurred satellite images. Some degradation effects such as atmospheric effects, clouds and their shadows, atmospheric aerosols, and fog remarkably decline the quality satellite images. Hence, some processes such as enhancement and edge detection in satellite images are challenging. One group of methods that can deal with these effects is fuzzy logic methods. Therefore, IFED method was applied in this work on the subimages of the Ikonos, Landsat 7, and SPOT 5 satellite images, contaminated by aforementioned effects. Such as most FED methods, IFED has two components: enhancement and edge detection. In this context, a six-step iterative method, using the if-then-else mechanism, was implemented on the images to perform fuzzy enhancement, and subsequently, edge detection was done. To evaluate the merit of the enhancement and select the best number of iterations, edge gray-value rate criterion was applied. The peak signal-to-noise ratio (PSNR) is applied for the quantitative evaluation of the IFED method. The results of IFED, in comparison with some prior edge detection methods, showed higher PSNR values and a high performance in the edge detection of the earth features in the blurred satellite images.

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A robust approach to image enhancement based on fuzzy logic

Cited by 177 publications

References 40 publications

Study on the Preprocessing Algorithm of Transmission Lines Video Monitoring Image

Study on the Preprocessing Algorithm of Transmission Lines Video Monitoring Image

Fuzzy Based Image Segmentation

Development of the iterative edge detection method applied on blurred satellite images: state of the art

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