Optimized Adaptive Fuzzy based Image Enhancement Techniques

Kaur, Taranbir; Sidhu, R.S.

doi:10.14257/ijsip.2016.9.1.02

Cited by 6 publications

(6 citation statements)

References 15 publications

(11 reference statements)

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“…Fuzzy Environment comprises of the elements corresponds to membership and uncertainty functions which are associated with image properties. In the medical image processing an expert knowledge is usually exploited to represent the uncertain data which can be described as fuzzy If-then logical rules [10]. A classical rule uses only logical symbols 1 and 0, whereas fuzzy rule consider a fuzzy set which represents the different levels of differentiation like low, medium and high values.…”

Section: Methodsmentioning

confidence: 99%

“…PSNR is a degree of enhancement used to estimate achievement attribute between the original and transformed images [20,10]. A high PSNR represents that quality is better than the input image.…”

Section: Image Performance Metricsmentioning

confidence: 99%

“…As Taranbir Kaur and Ravneet Kaur Sidhu demonstrated in 2016 [10], fuzzy logic can be used for enhancement with the help of a specified membership function. Mainly fuzzy logic is used to handle the images which are having vagueness and uncertainty.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Super Resolution of MR Brain Images Using Compressive Sensing and Fuzzy Logical Rules

Angalakurthi

Ramamurthy²

2021

Braz. arch. biol. technol.

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The proficiency of image processing is of extreme importance in perceiving and collecting information from the images, which includes the process of changing or interpreting existing images. In medical image processing, imaging with more accuracy plays a crucial role in better diagnosis or for the posterior analysis of treatment. Magnetic Resonance Imaging (MRI) is a medicinal creative tool for studying the internal structures and functionalities of human brain, knee, heart, liver, etc. Typical MR scans are essential now for better diagnosis but, limited resolution that is often inadequate for extracting detailed and reliable information. So, for the super resolution (SR) of MR brain images concepts of compressive sensing (CS) & fuzzy logical rules to improve data quality are proposed in this paper. Usually, reconstruction of an SR image is the formation of high resolution (HR) image which is obtained from one or few low resolution (LR) images. In the proposed method, with the help of compressive sensing a very limited number of images are considered even though it's a challenging task and fuzzy logical rules for a specific membership function are applied to improve the resolution of the image. To assess the performance of the proposal, different metrics are evaluated and achieved better results.

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Section: Methodsmentioning

confidence: 99%

Section: Image Performance Metricsmentioning

confidence: 99%

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Super Resolution of MR Brain Images Using Compressive Sensing and Fuzzy Logical Rules

Angalakurthi

Ramamurthy²

2021

Braz. arch. biol. technol.

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“…In the field of digital image enhancement and simulation of HFIE Algorithm lot of work is done and researchers have used different tools and techniques for measuring different parameters of the numerous types of images. Performance Evaluation of Fuzzy and Histogram Based Color Image Enhancement [4] Optimized Adaptive Fuzzy based Image Enhancement Techniques [5] FLBSMF [6] CLAHE [7] Underwater enhancement with HSV and HE image [8] are discussed in this section.…”

Section: Workmentioning

confidence: 99%

“…Image enhancement techniques based on optimized adaptive fuzzy [5] are primarily used to analyze the quality of existing image enhancement techniques such as histogram correction, adaptive histogram correction, and fuzzy image enhancement. It was observed that the value of the contrast parameter "K" in the fuzzy method was statically assumed to be 128.…”

Section: Enhancement Of Underwater Images [8] the V Transform Algorithm And Histogram Equalization Techniquesmentioning

confidence: 99%

Probabilistic Modeling of Histogram Based Fuzzy Image Enhancement Algorithm

Junejo

Bhatti

Memon

2022

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Probabilistic model checking is frequently used to examine stochastic behavior. Model-checking is important for examining before extensive simulations, in differing fields, including computer communications, networks, security, and biology. In this work performance of histogram-based fuzzy image enhancement (HFIE Algorithm). The fuzzy contrast enhancement algorithm based on histograms is used to improve low contrast colored photographs. K is the contrast intensification parameter calculated from the histogram. The RGB image is transformed into HSV to preserve the chromatic information in the original image. To improve the image, only the V component is stretched using the M and K parameters. HFIE Algorithm is analyzed by developing its Discrete-time Markov Chain (DTMC) model in the Probabilistic Symbolic Model Checker (PRISM). First, a labeled transition diagram showing the functionality of HFIE Algorithm is constructed, then the HFIE Algorithm model using the PRISM tool is developed. The expected time to convert RGB image to HSV, probability of achieving enhanced RGB image, and expected SNR are measured with the help of properties.

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