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Article
LEARNING TRADITIONAL FILTERS BASED ON IMAGE EXAMPLES

Author: Sarab M. Hameed
Journal: Al-Nahrain Journal of Science مجلة النهرين للعلوم ISSN: (print)26635453,(online)26635461 Year: 2007 Volume: 10 Issue: 1 Pages: 150-154
Publisher: Al-Nahrain University جامعة النهرين

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Abstract

This paper presents an approach for image filtering process depends on image examples. The approach involves of preparing a training images, in which a pair of images, with one image purported to be a filtered version of the other, as a training data. The filtered property is to be automatically learned and transferred to another target image. The basic idea behind learning approach is depended on luminance neighborhood statistics. Statistics pertaining to each pixel in the target pair are to be compared against statistics for every pixel in the source pair, and the closest match is to be found and its property (i.e., luminance information) is applied to the target image in order to create a filtered image. This approach is simple and provides results comparable to that obtained in image analogies of the Hertzmann et al.

يقدم هذا البحث طريقة لعملية ترشيح الصورة بالاعتماد على امثلة صور. تتضمن الطريقة تحضير صور التدريب، والتي هي زوج من الصور احدهما تمثل النسخة المرشحة للاخرى كبيانات تدريب. خاصية الترشيح ستتعلم بصورة اوتوماتكية وتتنقل هذه الخاصية الى صورة الهدف . إنّ الفكرةَ الأساسيةَ وراء طريقة التَعَلّم مُعتَمَدة على إحصائياتِ الجوارِ. الإحصائيات تَخْصُّ إلى كل نقطة شاشة في زوجِ الهدفَ سَتُقَارنُ ضدّ الإحصائياتِ لكل نقطة شاشة في الزوجِ المصدريِ، ونجد النظير الأقرب وخاصيته(معلومة الاضاءة) تطبق إلى صورةِ الهدفَ لكي تَكون صورةً مرشحة. هذه الطريقةِ بسيطةُ واعطت نتائج مشابه إلى تلك حَصلتْ عليها في تناظرات الصورةَ لـ Hertzmann واخرون.


Article
A GENETIC ALGORITHM FOR LEARNING IMAGE BLUR AND SHARPEN FILTERS

Author: Sarab M. Hameed
Journal: Al-Nahrain Journal of Science مجلة النهرين للعلوم ISSN: (print)26635453,(online)26635461 Year: 2007 Volume: 10 Issue: 2 Pages: 168-171
Publisher: Al-Nahrain University جامعة النهرين

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Abstract

In this paper, N-Queens problem was chosen to compare GA with PSO performance. GA with its own simple operators is stable in its performance under different search space sizes, while the PSO performs well in small search space size and its capabilities when space size becomes larger. This paper presents an approach for learning traditional image filters (blurring and sharpening). The concept of learning is based on the mechanism of Genetic algorithm (GA). By GA, filters applied on one source image can be learned and then used to process automatically another target image. By this way, blurring and sharpening can be implicitly deduced and applied without requiring to mathematically defining (i.e. explicitly) them. The proposed approach is simple and can provide good results; however, applying the filter directly is much more efficient.

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