Satellite image enhancement techniques — A comparative study

Pandey, Pratibha; Dewangan, Kranti Kumar; Dewangan, Deepak Kumar

doi:10.1109/icecds.2017.8389506

Cited by 20 publications

(2 citation statements)

References 9 publications

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“…Moreover, IE is useful for different tasks and fields that benefit from pre-processing an image to improve human or machine perception. Such fields are medical imagery, space imagery, bio-metric, photography, and video editing, among others [1][2][3]. Aesthetic improvement via IE is also one of the most popular uses of these techniques.…”

Section: Introductionmentioning

confidence: 99%

Towards Automatic Image Enhancement with Genetic Programming and Machine Learning

et al. 2022

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Image Enhancement (IE) is an image processing procedure in which the image’s original information is improved, highlighting specific features to ease post-processing analyses by a human or machine. State-of-the-art image enhancement pipelines apply solutions to fixed and static constraints to solve specific issues in isolation. In this work, an IE system for image marketing is proposed, more precisely, real estate marketing, where the objective is to enhance the commercial appeal of the images, while maintaining a level of realism and similarity with the original image. This work proposes a generic image enhancement pipeline that combines state-of-the-art image processing filters, Machine Learning methods, and Evolutionary approaches, such as Genetic Programming (GP), to create a dynamic framework for Image Enhancement. The GP-based system is trained to optimize 4 metrics: Neural Image Assessment (NIMA) technical and BRISQUE, which evaluate the technical quality of the images; and NIMA aesthetics and PhotoILike, that evaluate the commercial attractiveness. It is shown that the GP model was able to find the best image quality enhancement (0.97 NIMA Aesthetics), while maintaining a high level of similarity with the original images (Structural Similarity Index Measure (SSIM) of 0.88). The framework has better performance according to the image quality metrics than the off-the-shelf image enhancement tool and the framework’s isolated parts.

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Section: Introductionmentioning

confidence: 99%

Towards Automatic Image Enhancement with Genetic Programming and Machine Learning

et al. 2022

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“…Chronicobstructivepulmonarydisease(COPD)isoneoftheleadingchestdiseaseandthemajor reasonofworldwidedeathwhichcausedapproximately3.25deathsin2019 (WHO,2022a).Similarly, theriskofCOVID-19isfoundmoreintenseinthosepeoplewhohaveill-healthstatelikelungor heartdiseases (WHO,2022b).Theearlyandcautiousdiagnosisofmayreducethedeathrateand thereforemanylivescanbesaved.However,identifyingtheresponsiblevirusesarequitechallenging astheyhavevaryingstructuralrepresentationandmaychangeovertheperiodoftime.Anaffected butunidentifiedvirusinlungregionexaminationmayleadtothedeath.Nevertheless,chancesof manualmistakemayleadtounexpectedtreatmentduetothisshortdurationofinvestigation.One ofthebestwaystorecognizetheaffectedpatientsufferedfromCOVID-19infectionisthroughthe analysisofchestX-rayimages.Astherearealargenumberofpatientsinhospitals,itwouldbetime-consuminganddifficulttoexaminenumerousX-rayimages,soitcanbeveryusefultoutilizean artificialintelligence(AI)approachtoaccuratelyperformtherecognitionautomatically.Nowadays medicalfieldisalsobeingfacilitatedwiththeadvancementofseveralintelligenttechniques,which assiststheprecisedecisionmakingofdoctorsandphysician.Inthisdomain,severalimageprocessing andoptimizationapproacheshavebeenappliedtounderstandthedifferentdatapatternsinseveral applications (DeepakKumarDewangan;YogeshRathore,2011;BhattacharyaandDewangan,2015;Bhattacharya, Dewangan and Dewangan, 2018;Pandey, Dewangan and Dewangan, 2018;D. K. DewanganandSahu,2021;Sahuet al,2021),buttheirperformancehasbeenexperimentedagainst thestandardconditions.Inthisdirection,recentdevelopmentinmachineanddeeplearningtechniques have leveraged the performance of the AI based systems (A.…”

Section: Introductionmentioning

confidence: 99%

XHDLNet Classification of Virus-Borne Diseases for Chest X-Ray Images Using a Hybrid Deep Learning Approach

Choubey

Barde

Badholia

2022

International Journal of Software Innovation

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Various forms and symptoms of corona virus have been observed in human body especially in heart, chest and affects the respiratory system. In the initial phase, RT-PCR examination is applied to monitor the target disease, but suffers from low sensitivity and a laborious process. Apart from this, another mechanism for corona virus detection involves the analysis the CT image has become an imperative device for clinical judgment. However, manual investigation of such disease in numerous amounts of images is not the optimal approach. Additionally, recent advancement in artificial intelligence techniques have assisted medical diagnosis to identify the virus in a standard environment. In this work, the potential of such intelligence methods is analyzed and extended by considering the optimal feature extraction capability and proposes a hybrid approach in which three universal architectures namely: Inception V4, DenseNet 201 and Xception have been utilized which not only classify the corona virus disease but may also provide a pathway to apply similar method in other medical diagnosis.

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Image Enhancement Techniques to Modify an Image with Machine Learning Application

Das,

Sen,

Joardar

et al. 2024

How Machine Learning Is Innovating Today's World

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Satellite image enhancement techniques — A comparative study

Cited by 20 publications

References 9 publications

Towards Automatic Image Enhancement with Genetic Programming and Machine Learning

Towards Automatic Image Enhancement with Genetic Programming and Machine Learning

XHDLNet Classification of Virus-Borne Diseases for Chest X-Ray Images Using a Hybrid Deep Learning Approach

Image Enhancement Techniques to Modify an Image with Machine Learning Application

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