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Adaptive local fuzzy based region determination image enhancement techniques for non-uniform illumination and low contrast images / Abdullah Amer Mohammed Salih

Adaptive local fuzzy based region determination image enhancement techniques for non-uniform illumination and low contrast images_Abdullah Amer Mohammed Salih _E3_2018_MYMY
Peningkatan kontras setempat adalah satu kaedah bagi memperbaiki keterlihatan perincian setempat sesuatu imej dengan meningkatkan kontras di rantau tersebut. Kini, para penyelidik telah menunjukkan minat dalam menyelesaikan masalah pencahayaan yang tidak seragam. Walau bagaimanapun, kebanyakan teknik ini membahagikan imej kepada dua bahagian sahaja iaitu kawasan yang terdedah-lebih dan terdedah-kurang, dan cuba meningkatkon kontras rendah pada kedua-dua rantau menggunakan pendekatan yang sama. Tetapi, semua kaedah ini tidak mantap dan telah direka khusus untuk menyelesaikan masalah tertentu pada sesuatu masa. Kekangan ini telah memotivasikan kajian ini dengan mencadangkan teknik baharu bagi menyelesaikan masalah yang diyatakan. Pada mulanya, Kaedah Penentuan Kawasan Berasaskan Pendedahan Tempatan Adaptif (ALEBRD) dicadang untuk menentukan dan membahagikan imej kepada tiga rantau iaitu kawasan yang terdedah-lebih, terdedah-kurang dan terdedah-baik. Keputusan menunjukkan kaedah ALEBRD yang dicadang menghasilkan prestasi penentuan rantau yang lebih baik berbanding kaedah konvensional. Berdasarkan analisis kualitatif, ia dapat menentukan ketiga-tiga rantau dengan ketepatan yang tinggi. Kemudian, kontras setiap rantau akan dipertingkatkan menggunakan teknik peningkatan kontras setempat baharu yang dinamakan Peningkatan Kontras Setempat Pendedahan Kabur Adaptif (AFELCE). Kaedah AFELCE yang dicadang dengan direka bentuk khusus untuk meningkatkan kontras setiap rantau menggunakan kaedah berbeza. Teknik AFELCE yang dicadangkan berjaya meningkatkan kontras 300 imej berkontras rendah dan berilluminasi tidak seragam yang diambil dari tiga pangkalan data imej iaitu imej-imej piawai, bawah air dan sperma manusia mikroskopik. Kaedah AFELCE yang dicadangkan mengatasi kaedah terkini secara kualitatif dan kuantitatif. Secara kualitatif, kaedah AFELCE yang dicadangkan berjaya meningkatkan kontras imej tersebut dengan menghasilkan imej pencahayaan yang lebih seragam dengan kontras yang tinggi. Secara kuantitatif, kaedah AFELCE yang dicadangkan memberi purata tertinggi untuk parameter Entropi (E), Pengukur Peningkatan (EME) dan Indeks Universal Kualiti Imej (UIQI) bagi pangkalan data imej piawai dengan nilai masing-masing 7.582, 42.75 dan 0.94. Keputusan yang sama diperoleh untuk imej bawah laut dengan ia menghasilkan purata nilai tertinggi E, EME dan UIQI dengan nilai masing-masing 7.124, 41.13 dan 0.89. Sementara itu, bagi pangkalan data imej mikroskopik sperma manusia, ia memberikan nilai-nilai tertinggi dalam E dan EME iaitu masing-masing 7.602 dan 42.51. _______________________________________________________________________ Local contrast enhancement is an approach to improve the local visibility detail of an image by increasing the contrast in local regions. Recently, researchers have shown an interest in solving the issue of non-uniform illumination. However, most of these techniques divide the image into two parts only namely over-exposed and under-exposed regions and try to enhance the poor contrast in both regions using same approach. However, these methods are not robust and they are specifically designed to solve a specific problem at one time. This limitation has motivated this study to propose a new technique to solve the abovementioned problems. In the beginning, Adaptive Local Exposure Based Region Determination (ALEBRD) method is proposed to determine and divide the image into three regions namely under-exposed, over-exposed, and well-exposed regions. The results show that the proposed ALEBRD method produced better region determination performance than the other state-of-the-art methods. Based on the qualitative analysis, it could determine those three regions with high accuracy. After that, contrast of each region will be enhanced using a new local contrast enhancement technique called Adaptive Fuzzy Exposure Local Contrast Enhancement (AFELCE). The proposed AFELCE method is specifically designed to enhance the contrast of each region using different approaches. The proposed AFELCE technique successfully improves the contrast of 300 low-contrast and non-uniform illumination images, taken from three different databases namely standard, underwater, and microscopic human sperm images. The proposed AFELCE method qualitatively and quantitatively outperforms the state-of-the-art methods,. Qualitatively, the proposed AFELCE method has successfully enhanced the contrast of those images by producing more uniform illumination images with high contrast. Quantitatively, the proposed AFELCE method produces the highest average of Entropy (E), Measure of Enhancement (EME) and Universal Image Quality Index (UIQI) for the standard image database with values of 7.582, 42.75 and 0.94 respectively. The similar results obtained for the underwater database images, where it produces the highest average of E, EME and UIQI values with 7.124, 41.13 and 0.89 respectivley. While for the microscopic human sperm image database, it produces the highest values for E and EME with values of 7.602 and 42.51 respectively, and . This study is suitable to be applied to a real time applications. Based on the good results obtained for standard, underwater, and microscopic human sperm images, the developed system has high potential and suitable to be applied to a real time applications.
Contributor(s):
Abdullah Amer Mohammed Salih - Author
Primary Item Type:
Thesis
Identifiers:
Accession Number : 875008937
Language:
English
Subject Keywords:
determination image ; non-uniform illumination ; low contrast images
Sponsor - Description:
Pusat Pengajian Kejuruteraan Elektrik & Elektronik -
First presented to the public:
8/1/2018
Original Publication Date:
11/3/2020
Previously Published By:
Universiti Sains Malaysia
Place Of Publication:
School of Electrical & Electronic Engineering
Citation:
Extents:
Number of Pages - 207
License Grantor / Date Granted:
  / ( View License )
Date Deposited
2020-11-03 11:07:28.112
Submitter:
Mohamed Yunus Yusof

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