ODAKLAMA DERİNLİĞİNİN ARTIRILMASINDA DERİN ÖZELLİKLERİN ODAKLAMA DEĞERLERİNİN ÇIKARILMASINDAKİ ETKİLERİNİN İNCELENMESİ

Year 2023, , 917 - 930, 16.12.2023

Sibel Danışmaz , Sümeyye Nur Emir , Hülya Doğan , Ramazan Özgür Doğan

https://doi.org/10.31796/ogummf.1299670

Abstract

Mikroskobik sistemlerde var olan odaklama derinliğinden dolayı numunenin tüm alanının odaklandığı görüntü elde etmek imkânsız olabilmektedir. Bu durum, mikroskobik sistemlerde görüntü işleme ve yapay zekâ algoritmaları kullanılarak gerçekleştirilen sınıflandırma, bölütleme, hizalama (registration), panoramik birleştirme (stitching) gibi uygulamalarının başarılarını olumsuz yönde etkilemektedir. Literatürde numunenin tüm alanının odaklandığı görüntü elde etmek için odaklama derinliğinin artırılması yaklaşımları geliştirilmektedir. Literatür çalışmaları, bu yaklaşımların, görüntülerdeki eğrilerin ve kenarların düşük kesinlikte karakterizasyonu, daha yüksek koşma süresi ve incelenen numuneye ve kullanılan mikroskoba göre performans değişimi gibi çeşitli kısıtlamalara sahip olduklarını ortaya koymaktadır. Ek olarak, bu yaklaşımlar odaklama bilgilerini genelde görüntülerin gri seviye değerlerini kullanarak hesaplamaktadırlar. Bu çalışmada bu kısıtlamaları minimize etmek için yeni bir odaklama derinliğinin artırılması yaklaşımı geliştirilmekte ve odaklama derinliğinin artırılmasında derin özelliklerin odaklama değerlerinin çıkarılmasındaki etkileri incelenmektedir. Çalışmada elde edilen sonuçlar derin özelliklerin piksellerin odaklama değerlerini hesaplamada gri seviye değerlerine göre daha etkin olduğunu göstermektedir.

Keywords

Odaklama derinliği, Odaklama derinliğinin artırılması, Derin öğrenme, Odaklama ölçüm operatörü

INVESTIGATION OF EFFECTS OF DEEP FEATURES ON FOCUS VALUES EXTRACTION IN EXTENDED DEPTH OF FOCUS

Abstract

Due to the focusing depth in microscopic systems, it may be impossible to obtain an image in which the entire area of the sample is focused. This situation negatively affects the success of applications such as classification, segmentation, registration, panoramic stitching, which are performed using image processing and artificial intelligence algorithms in microscopic systems. In the literature, approaches are developed to increase the focusing depth to obtain an image in which the entire area of the sample is focused. Literature studies reveal that these approaches have several limitations, such as low-precision characterization of curves and edges in images, higher running time, and performance variation according to the sample examined and the microscope used. In addition, these approaches often calculate focusing information using the gray level values of the images. In this study, a new approach to increasing the focusing depth is developed in order to minimize these limitations and the effects of deep features on the extraction of focusing values in increasing the focusing depth are examined. The results obtained in the study show that the deep features are more effective in calculating the focusing values of the pixels than the gray level values.

Keywords

Depth of focus, Extended depth of focus, Deep learning, Focus measurement operator

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