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Advanced Image Processing Using Histogram Equalization and Android Application Implementation
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Now a days the conditions at which the image taken may lead to near zero visibility for the human eye. They may usually due to lack of clarity, just like effects enclosed on earth’s atmosphere which have effects upon the images due to haze, fog and other day light effects. The effects on such images may exists, so useful information taken under those scenarios should be enhanced and made clear to recognize the objects and other useful information. To deal with such issues caused by low light or through the imaging devices experience haze effect many image processing algorithms were implemented. These algorithms also provide nonlinear contrast enhancement to some extent. We took pre-existed algorithms like SMQT (Successive mean Quantization Transform), V Transform, histogram equalization algorithms to improve the visual quality of digital picture with large range scenes and with irregular lighting conditions. These algorithms were performed in two different method and tested using different image facing low light and color change and succeeded in obtaining the enhanced image. These algorithms helps in various enhancements like color, contrast and very accurate results of images with low light. Histogram equalization technique is implemented by interpreting histogram of image as probability density function. To an image cumulative distribution function is applied so that accumulated histogram values are obtained. Then the values of the pixels are changed based on their probability and spread over the histogram. From these algorithms we choose histogram equalization, MATLAB code is taken as reference and made changes to implement in API (Application Program Interface) using JAVA and confirms that the application works properly with reduction of execution time.

Place, publisher, year, edition, pages
2016. , 50 p.
Keyword [en]
Application program interface, Histogram equalization, Probability density function, Successive mean quantization transform.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-13735OAI: oai:DiVA.org:bth-13735DiVA: diva2:1063170
Subject / course
ET2566 Master's Thesis (120 credits) in Electrical Engineering with emphasis on Signal processing
Educational program
ETASX Master of Science Programme in Electrical Engineering with emphasis on Signal Processing
Supervisors
Examiners
Available from: 2017-01-09 Created: 2017-01-09 Last updated: 2017-01-09Bibliographically approved

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fulltext(7215 kB)282 downloads
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35d4d9bf3af4dd1892dbed32650d31546bfeecc274dd428c6fb30d72d6fd31dfb30ebb94d76ee83dd810dd6fa7eea129de5087213793ff96bcd3521d42217783
Type fulltextMimetype application/pdf

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Department of Applied Signal Processing
Signal Processing

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
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  • Other locale
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Output format
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