Fuzzy Logic Applied to Improvement of Image Resolution using Gaussian Membership Functions

Souverville, Samuel; Rosales, Jorge A.; Gallegos, Francisco; Dehesa, Mario; Hernández, Isabel V.; Lozano, Lucero V.

doi:10.13053/rcs-102-1-7

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Most images typically suffer from imperfections such as poor contrast and low-resolution contrast, and imaging artifacts. Enhancement mechanisms for images such as global stretching and histogram equalization do not all the time give good results, particularly for images with significant variance [24]. According to the importance of the image enhancement principles, many approaches have been yielded in the literature.…”

Section: Image Enhancement By Using Fuzzymentioning

confidence: 99%

Enhancement of medical images using fuzzy logic

Fadil

Al-Bander²,

Radhi³

2021

IJEECS

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Image enhancement is one of the most critical subjects in computer vision and image processing fields. It can be considered as means to enrich the perception of images for human viewers. All kinds of images typically suffer from different problems such as weak contrast and noise. The primary purpose of image enhancement is to change an image's visual appearance. Many algorithms have recently been proposed for enhancing medical images. Image enhancement is still deemed a challenging task. In this paper, the fuzzy c-means clustering (FCM) technique is utilized to enhance the medical images. The method of enhancement consists of two stages. The proposed algorithm conducts a cluster test on the image pixels. It then increases the difference of gray level between the diverse objects to accomplish the enhancement purpose of the medical images. The experimental results have been tested using various images. The algorithm enhanced the small target of the image to a reasonable limit and revealed favorable performance. The results of image enhancement techniques were evaluated by using terms of different criteria such as peak signal to noise ratio (PSNR), mean square error (MSE) and average information contents (AIC), showing promising performance.

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Section: Image Enhancement By Using Fuzzymentioning

confidence: 99%

Enhancement of medical images using fuzzy logic

Fadil

Al-Bander²,

Radhi³

2021

IJEECS

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“…A fuzzy logical rule can be of the form as a counterfactual statement which is characterized with linguistic variables and linguistic values. Usually it can be defined with antecedent and consequent operations as [15]. IF x is A then y is B, Where x and y are linguistic variables and A, B are linguistic values.…”

Section: Image Resolution Enhancement Process Flowmentioning

confidence: 99%

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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“…A prominent problem surfaced by [163] refers to what [163] deemed as the "hallucination problem", although we presume that this may not be a significant concern in computational nanoscopy, especially where an extensive dataset (comprising varied samples which addresses the issue being investigated) is utilized, except where highly specific post-acquisition image processing and analysis protocols (such as feature detection and object tracking) are desired, in which instance, an over-fitted DNN (prone to such hallucination issues) may be utilized. In addition, fuzzy logic approaches (such as that employed in [164], [165] & [166]) may also contribute to network hallucination (despite outperforming even the state-of-the-art super-resolution methods as mentioned in [166]), since fuzzy logic approximates the mapping of image patches to highly-resolved images, while this approximation may (at times) result in incorrect image mappings. In this respect, it would also be noteworthy to mention that future approaches in computational super-resolution microscopy should seek to integrate both deconvolution and deep learning principles, by using deep learning to accurately discern the optical PSF and noise of an optical system, which may vary spatiotemporally across the sample, especially in living cells.…”

Section: Current Limitations and Potential Future Advancements Imentioning

confidence: 99%

Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical Microscopy

et al. 2020

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The field of optical nanoscopy, a paradigm referring to the recent cutting-edge developments aimed at surpassing the widely acknowledged 200nm-diffraction limit in traditional optical microscopy, has gained recent prominence & traction in the 21 st century. Numerous optical implementations allowing for a new frontier in traditional confocal laser scanning fluorescence microscopy to be explored (termed super-resolution fluorescence microscopy) have been realized through the development of techniques such as stimulated emission and depletion (STED) microscopy, photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM), amongst others. Nonetheless, it would be apt to mention at this juncture that optical nanoscopy has been explored since the mid-late 20 th century, through several computational techniques such as deblurring and deconvolution algorithms. In this review, we take a step back in the field, evaluating the various in silico methods used to achieve optical nanoscopy today, ranging from traditional deconvolution algorithms (such as the Nearest Neighbors algorithm) to the latest developments in the field of computational nanoscopy, founded on artificial intelligence (AI). An insight is provided into some of the commercial applications of AI-based super-resolution imaging, prior to delving into the potentially promising future implications of computational nanoscopy. This is facilitated by recent advancements in the field of AI, deep learning (DL) and convolutional neural network (CNN) architectures, coupled with the growing size of data sources and rapid improvements in computing hardware, such as multi-core CPUs & GPUs, low-latency RAM and hard-drive capacities.

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Fuzzy Logic Applied to Improvement of Image Resolution using Gaussian Membership Functions

Cited by 4 publications

References 7 publications

Enhancement of medical images using fuzzy logic

Enhancement of medical images using fuzzy logic

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

Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical Microscopy

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