Yüz Görüntüleri İçin Yeni Bir Süper Çözünürlük Yaklaşımı ve Farklı Görüntü Kalitesindeki Veri Setlerinin Başarı Performansı Üzerindeki Etkileri

Yıl 2023, Cilt: 5 Sayı: 1, 1 - 8, 30.04.2023

Fatih Mehmet Şenalp

https://doi.org/10.46387/bjesr.1188923

Öz

Çözünürlük kavramı, çeşitli bilgisayarlı görü uygulamaları için büyük önem arz etmektedir. Son yıllarda donanımsal ilerlemeler sayesinde görüntü çözünürlüklerini artırmaya yönelik süper çözünürlük uygulamaları araştırmacıların odak noktası haline gelmiştir. Bu çalışmada ise yeni bir derin öğrenme tabanlı süper çözünürlük modeli (SISRGAN) önerilmiştir. Ayrıca, süper çözünürlük uygulamaları için CelebA veri setinden farklı kalite seviyelerinde üç farklı veri seti oluşturulmuştur. Çalışmalar sonucunda elde edilen sonuçlar görüntü kalite metrikleri (tepe sinyal gürültü oranı ve yapısal benzerlik indeksi) kullanılarak literatürde yer alan önemli modeller ile karşılaştırılmıştır. Önerilen derin ağ modelinin hem görsel kalitedeki iyileşme hem de metrik değerleri açısından daha üstün bir başarı ortaya koymuştur. Bununla birlikte, süper çözünürlüklü görüntünün oluşturulacağı düşük çözünürlüklü görüntü kalitesinin başarıyı doğrudan etkilediği görülmüştür.

Anahtar Kelimeler

Süper Çözünürlük, Derin Öğrenme, Görüntü Kalite Metrikleri, Farklı Görüntü Kalitesindeki Veri Setleri

A New Super Resolution Approach for Face Images and the Effects of the Datasets of Different Image Quality on the Success Performance

Öz

The term of resolution is of great importance for various computer vision applications. In recent years, super resolution applications to increase image resolutions have become the focus of researchers, thanks to hardware advances. In this paper, a new deep learning-based super resolution model (SISRGAN) is proposed. In addition, three different datasets with different quality levels were created from the CelebA dataset for super resolution applications. The results obtained as a result of the studies were compared with the state-of-the-art models in the literature using image quality metrics (peak signal to noise ratio and structural similarity index). The proposed deep network model showed a superior success in terms of both visual quality improvement and metric values. However, it has been seen that the low resolution image quality in which the super resolution image will be created directly affects the success.

Anahtar Kelimeler

Super Resolution, Deep Learning, Image Quality Metrics, Datasets of Different Image Quality

Kaynakça

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