Gray-level image enhancement using differential evolution optimization algorithm

Sarangi, Partha Pratim; Mishra, Bhabani Shankar Prasad; Majhi, Banshidhar; Dehuri, Satchidananda

doi:10.1109/spin.2014.6776929

Cited by 26 publications

(9 citation statements)

References 7 publications

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“…These may consist of image enhancement, segmentation, recognition, and interpretation [47]. Enhancement refers to the optimization of the images to improve their quality; it may include sharpening, contrast adjustment, and denoising, among others [48]. Segmentation is the division of the images or frames in specific regions of interest.…”

Section: Computer Vision Techniquesmentioning

confidence: 99%

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

et al. 2019

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Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

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Section: Computer Vision Techniquesmentioning

confidence: 99%

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

et al. 2019

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“…Again With a differential evolution optimization technique, [12] attempted to implement gray level images by demonstrating the adaptability and effectiveness for locating global best solutions to details of an image. Their technique which adopt a transformation function of parameterized intensity as an objective function resulted in achieving desired image.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Gray Level Image Transformation Techniques

Baidoo¹

2018

IJMECS

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Gray level transformation is a significant part of image enhancement techniques which deal with images composed of pixels. The outcomes of this process can be either images or a set of representative characteristics. It effects is simple but complicated in its implementation. Recently much work is completed in the field of images enhancement with varying observable techniques. This paper describes how to enhance an image using different gray level techniques and a demonstration of its implementation. PPI Analyzer, a kind of software created to implement the various techniques is based on explosive phenomenon of MATLAB. The implemented program with interactive interface to allow for relaxed modification, presented encouraging results.

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“…In 2014, P.P.Sarangi et al [26] suggested contrast enhancement of an image by gray level modification using parameterized intensity transformation function that is considered as an objective function. The task of D.E was to adapt the parameters of the transformation function by maximizing the objective function criterion.…”

Section: A Image Contrast Enhancementmentioning

confidence: 99%

Recent Trends and Techniques in Image Enhancement using Differential Evolution- A Survey

Choudhary¹,

Gupta²

2017

IJARCSSE

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Abstract-The Differential Evolution algorithm represents an adaptive search process for solving engineering and machine learning optimization problems. Performance of this algorithm rely on its parameter setting. These parameters are usually constant during the entire search space. Recently each and every field of research is utilizing the properties of DE. One of the very active area of research is image enhancement which is growing very fast. Many researchers have proposed various research papers with enhancement of various parameter by combining DE with different image enhancement techniques. This paper reviews several approaches for contrast enhancement in spatial domain as well as in frequency domain by using DE. The survey leads to the conclusion that field of DE is growing fast and improving the contrast using DE will help to solve many complex problems in Image Processing.

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Gray-level image enhancement using differential evolution optimization algorithm

Cited by 26 publications

References 7 publications

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Implementation of Gray Level Image Transformation Techniques

Recent Trends and Techniques in Image Enhancement using Differential Evolution- A Survey

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