Görüntüden Görüntüye Dönüşüm ve Görüntü Sentezleme Yapan Üretici Çekişmeli Ağların İncelenmesi

Year 2021, Issue: 26 - Ejosat Special Issue 2021 (HORA), 53 - 60, 31.07.2021

Sara Altun , Muhammed Fatih Talu

https://doi.org/10.31590/ejosat.949116

Abstract

Görüntüden görüntüye dönüşüm işlemi; görüntü işleme, bilgisayar grafikleri ve bilgisayarla görme alanındaki problem çözümlerinde kullanılmaktadır. Görüntüden görüntüye dönüşüm, belirli bir girdinin bir görsel temsilini başka bir temsille eşleştirmeyi öğrenmeyi gerektirmektedir. Üretici çekişmeli ağlarla (GAN'lar) görüntüden görüntüye dönüşüm yoğun bir şekilde incelenmiştir. Ve incelenen yöntemler çok modlu, süper çözünürlüklü, nesne dönüştürme ile ilgili dönüşüm vb. gibi birçok kısma uygulanmıştır. Ancak, görüntüden görüntüye dönüşüm teknikleri, istikrarsızlık ve çeşitlilik eksikliği gibi bazı sorunlardan muzdariptir. Bu çalışmada, GAN algoritmalarına ve türevlerine dayanan görüntüden görüntüye dönüşüm yöntemlerine kapsamlı bir genel bakış açısı sağlama amaçlanmıştır. Ayrıca, görüntüden görüntüye dönüşüm teknikleri tartışılıp ve analiz edilmektedir. Son olarak gelecekteki araştırılan yöntemler özetlenmekte ve tartışılmaktadır.

Keywords

Görüntüden görüntüye dönüşüm, üretici çekişmeli ağlar, derin öğrenme

Review of Generative Adversarial Networks for Image-to-Image Translation and Image Synthesis

Abstract

Image to image translation process is used in image processing, computer graphics and computer vision problem solving. Image-to-image translation requires learning to match one visual representation of a given input to another representation. Image-to-image translation has been extensively studied with producer adversarial networks (GANs). And the methods studied are multimodal, super tool, transformation into an object, and so on. It has been applied to many parts such as. However, it suffers from some issues such as image-to-image formatting, instability, and lack of variety. This article is intended to provide a comprehensive overview of the image translation methods according to GAN algorithms and their derivatives. In addition, its image-to-image formation is discussed and analyzed. Finally, future research methods are summarized and discussed.

Keywords

Image-to-image translation, generative adversarial networks, deep learning.

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