{"id":29828,"date":"2025-09-20T10:40:22","date_gmt":"2025-09-20T07:40:22","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/srgan-super-cozunurluklu-uretici-cekismeli-aglar\/"},"modified":"2025-09-20T10:40:22","modified_gmt":"2025-09-20T07:40:22","slug":"srgan-super-cozunurluklu-uretici-cekismeli-aglar","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/srgan-super-cozunurluklu-uretici-cekismeli-aglar\/","title":{"rendered":"SRGAN: S\u00fcper \u00c7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc \u00dcretici \u00c7eki\u015fmeli A\u011flar"},"content":{"rendered":"

# SRGAN ile G\u00f6r\u00fcnt\u00fc S\u00fcper \u00c7\u00f6z\u00fcn\u00fcrl\u00fc\u011f\u00fc: Ad\u0131m Ad\u0131m Uygulamal\u0131 K\u0131lavuz<\/p>\n

D\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc bir foto\u011fraf\u0131n\u0131z var ve onu olabildi\u011fince net hale getirmek istiyorsunuz. Bu, eski aile foto\u011fraflar\u0131n\u0131 canland\u0131rmaktan, g\u00fcvenlik kameralar\u0131ndan elde edilen g\u00f6r\u00fcnt\u00fcleri iyile\u015ftirmeye kadar bir\u00e7ok alanda hayati \u00f6nem ta\u015f\u0131yor. \u0130\u015fte tam bu noktada SRGAN (Super-Resolution Generative Adversarial Networks) devreye giriyor. Bu makalede, SRGAN’\u0131n temellerinden uygulamal\u0131 \u00f6rneklerine, performans optimizasyonuna ve ileri d\u00fczey tekniklerine kadar her \u015feyi ele alaca\u011f\u0131z.<\/p>\n

SRGAN Nedir ve Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/strong><\/p>\n

SRGAN, d\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc bir g\u00f6r\u00fcnt\u00fcy\u00fc y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc bir kar\u015f\u0131l\u0131\u011f\u0131na d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in derin \u00f6\u011frenme ve \u00f6zellikle Generative Adversarial Networks (GAN) mimarisini kullanan bir y\u00f6ntemdir. GAN’ler, birbirine kar\u015f\u0131 yar\u0131\u015fan iki a\u011fdan olu\u015fur: bir \u00fcretici (generator)<\/strong> ve bir ayr\u0131mc\u0131 (discriminator)<\/strong>. \u00dcretici, d\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc bir g\u00f6r\u00fcnt\u00fcden y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc bir g\u00f6r\u00fcnt\u00fc olu\u015ftururken, ayr\u0131mc\u0131, \u00fcreticinin olu\u015fturdu\u011fu g\u00f6r\u00fcnt\u00fcn\u00fcn ger\u00e7ek mi yoksa sahte mi oldu\u011funu belirlemeye \u00e7al\u0131\u015f\u0131r. Bu rekabet\u00e7i s\u00fcre\u00e7, \u00fcreticinin giderek daha ger\u00e7ek\u00e7i y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc g\u00f6r\u00fcnt\u00fcler \u00fcretmesini sa\u011flar. SRGAN, \u00f6zellikle ger\u00e7ek\u00e7i detaylar\u0131n ve dokular\u0131n olu\u015fturulmas\u0131nda di\u011fer s\u00fcper \u00e7\u00f6z\u00fcn\u00fcrl\u00fck y\u00f6ntemlerine g\u00f6re \u00fcst\u00fcn performans g\u00f6sterir.<\/p>\n

SRGAN’\u0131n Temel Bile\u015fenleri: \u00dcretici ve Ayr\u0131mc\u0131<\/strong><\/p>\n

\u00dcretici a\u011f:<\/strong> Genellikle derin konvol\u00fcsyonel sinir a\u011flar\u0131ndan (CNN) olu\u015fur ve d\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc bir g\u00f6r\u00fcnt\u00fcy\u00fc girdi olarak alarak y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc bir g\u00f6r\u00fcnt\u00fc \u00fcretir. Bu a\u011f, \u00f6zellik \u00e7\u0131kar\u0131m\u0131 ve y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc g\u00f6r\u00fcnt\u00fcn\u00fcn sentezlenmesi i\u00e7in birden fazla katmandan olu\u015fur. ResNet (Residual Network) gibi mimariler, bilgi kayb\u0131n\u0131 \u00f6nlemek ve e\u011fitimi kolayla\u015ft\u0131rmak i\u00e7in s\u0131k\u00e7a kullan\u0131l\u0131r.<\/p>\n

Ayr\u0131mc\u0131 a\u011f:<\/strong> Ger\u00e7ek y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc g\u00f6r\u00fcnt\u00fcler ve \u00fcreticinin \u00fcretti\u011fi g\u00f6r\u00fcnt\u00fcler aras\u0131nda ayr\u0131m yapmaya \u00e7al\u0131\u015f\u0131r. Bu a\u011f da bir CNN olup, g\u00f6r\u00fcnt\u00fcn\u00fcn ger\u00e7ek olup olmad\u0131\u011f\u0131na dair bir olas\u0131l\u0131k puan\u0131 \u00fcretir. Ayr\u0131mc\u0131n\u0131n performans\u0131, \u00fcreticinin ger\u00e7ek\u00e7i g\u00f6r\u00fcnt\u00fcler \u00fcretme yetene\u011fini do\u011frudan etkiler.<\/p>\n

SRGAN’\u0131 Anlamak \u0130\u00e7in Ad\u0131m Ad\u0131m \u00d6\u011frenme Yol Haritas\u0131<\/strong><\/p>\n

# Yeni Ba\u015flayanlar \u0130\u00e7in:<\/strong><\/p>\n

Ad\u0131m 1: Temel Kavramlar\u0131 \u00d6\u011frenme:<\/strong> Derin \u00f6\u011frenme, konvol\u00fcsyonel sinir a\u011flar\u0131 (CNN) ve GAN’lerin temel prensiplerini anlamak \u00f6nemlidir. Bu konular hakk\u0131nda online kaynaklar ve dersler mevcuttur.<\/p>\n

Ad\u0131m 2: Basit bir SRGAN uygulamas\u0131:<\/strong> TensorFlow veya PyTorch gibi derin \u00f6\u011frenme k\u00fct\u00fcphanelerini kullanarak basit bir SRGAN modeli e\u011fitmeyi deneyin. Haz\u0131rda bulunan kod \u00f6rneklerini kullanarak ba\u015flayabilir ve daha sonra kendi verilerinizle deneyler yapabilirsiniz. \u00d6rne\u011fin, k\u00fc\u00e7\u00fck bir veri setiyle ba\u015flay\u0131p, modeli daha sonra daha b\u00fcy\u00fck bir veri setine geni\u015fletebilirsiniz.<\/p>\n

Basit Kod \u00d6rne\u011fi (PyTorch – Kavramsal):<\/strong><\/p>\n

# Bu sadece kavramsal bir \u00f6rnektir. Ger\u00e7ek bir uygulama daha karma\u015f\u0131kt\u0131r.\nimport torch\nimport torch.nn as nn\n\n# \u00dcretici a\u011f\u0131 (basitle\u015ftirilmi\u015f)\nclass Generator(nn.Module):\n  def __init__(self):\n    super(Generator, self).__init__()\n    # ... Katmanlar ...\n\n# Ayr\u0131mc\u0131 a\u011f\u0131 (basitle\u015ftirilmi\u015f)\nclass Discriminator(nn.Module):\n  def __init__(self):\n    super(Discriminator, self).__init__()\n    # ... Katmanlar ...\n\n# ... E\u011fitim d\u00f6ng\u00fcs\u00fc ...<\/code><\/pre>\n
# Orta Seviye:<\/strong><\/p>\n
Ger\u00e7ek Hayat \u00d6rne\u011fi:<\/strong>  Eski foto\u011fraflar\u0131n restorasyonu, g\u00fcvenlik kameras\u0131 g\u00f6r\u00fcnt\u00fclerinin iyile\u015ftirilmesi veya t\u0131bbi g\u00f6r\u00fcnt\u00fclemede \u00e7\u00f6z\u00fcn\u00fcrl\u00fck art\u0131r\u0131m\u0131 gibi ger\u00e7ek d\u00fcnya senaryolar\u0131nda SRGAN’\u0131 uygulay\u0131n.  Farkl\u0131 veri setleri \u00fczerinde deneyler yaparak modelin performans\u0131n\u0131 de\u011ferlendirin.<\/p>\n
Optimizasyon \u0130pu\u00e7lar\u0131:<\/strong>  \u00d6\u011frenme oran\u0131, batch boyutu, kay\u0131p fonksiyonu gibi hiperparametreleri ince ayar yaparak modelin performans\u0131n\u0131 iyile\u015ftirin.  Modelin a\u015f\u0131r\u0131 \u00f6\u011frenmesini (overfitting) \u00f6nlemek i\u00e7in d\u00fczenleme teknikleri (regularization) kullan\u0131n.  Veri art\u0131r\u0131m\u0131 (data augmentation) teknikleri ile e\u011fitim verilerinizi geni\u015fletebilirsiniz.<\/p>\n
# \u0130leri D\u00fczey:<\/strong><\/p>\n
Performans Analizi:<\/strong>  PSNR (Peak Signal-to-Noise Ratio) ve SSIM (Structural Similarity Index) gibi metrikleri kullanarak modelin performans\u0131n\u0131 ayr\u0131nt\u0131l\u0131 olarak analiz edin.  Farkl\u0131 mimariler, kay\u0131p fonksiyonlar\u0131 ve optimizasyon algoritmalar\u0131 aras\u0131ndaki performans farklar\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131r\u0131n.<\/p>\n
Edge Case’ler:<\/strong>  G\u00fcr\u00fclt\u00fcl\u00fc g\u00f6r\u00fcnt\u00fcler, bulan\u0131k g\u00f6r\u00fcnt\u00fcler veya belirli t\u00fcrdeki g\u00f6r\u00fcnt\u00fcler \u00fczerinde modelin performans\u0131n\u0131 de\u011ferlendirin.  Modelin s\u0131n\u0131rlamalar\u0131n\u0131 belirleyin ve bu s\u0131n\u0131rlamalar\u0131 a\u015fmak i\u00e7in stratejiler geli\u015ftirin.  \u00d6rne\u011fin, belirli bir g\u00f6r\u00fcnt\u00fc t\u00fcr\u00fc i\u00e7in \u00f6zel olarak e\u011fitilmi\u015f bir SRGAN modeli geli\u015ftirebilirsiniz.<\/p>\n
SRGAN Uygulamas\u0131: Ad\u0131m Ad\u0131m Uygulamal\u0131 K\u0131lavuz<\/strong><\/p>\n
Bu b\u00f6l\u00fcmde, SRGAN’\u0131 kullanarak d\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc bir g\u00f6r\u00fcnt\u00fcy\u00fc y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc hale getirme s\u00fcrecini ad\u0131m ad\u0131m a\u00e7\u0131klayaca\u011f\u0131z.  Bu s\u00fcre\u00e7, veri toplama, model e\u011fitimi ve son de\u011ferlendirmeyi kapsar.<\/p>\n
Ad\u0131m 1: Veri Toplama ve Haz\u0131rlama:<\/strong>  Y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc ve d\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc g\u00f6r\u00fcnt\u00fc \u00e7iftlerinden olu\u015fan bir veri seti toplaman\u0131z gerekir.  Bu veriler, SRGAN modelini e\u011fitmek i\u00e7in kullan\u0131lacakt\u0131r.  Veri setini haz\u0131rlama a\u015famas\u0131nda, g\u00f6r\u00fcnt\u00fcleri uygun boyuta \u00f6l\u00e7ekleyin ve normalle\u015ftirin.<\/p>\n
Ad\u0131m 2: Model Se\u00e7imi ve E\u011fitimi:<\/strong>  \u00d6nceden e\u011fitilmi\u015f bir SRGAN modeli kullanabilir veya kendi modelinizi e\u011fitebilirsiniz.  E\u011fitim s\u00fcreci, se\u00e7ti\u011finiz derin \u00f6\u011frenme k\u00fct\u00fcphanesine ba\u011fl\u0131 olarak de\u011fi\u015fir.  E\u011fitim s\u0131ras\u0131nda, modelin performans\u0131n\u0131 d\u00fczenli olarak izleyin ve gerekti\u011finde hiperparametreleri ayarlay\u0131n.<\/p>\n
Ad\u0131m 3:  Sonu\u00e7lar\u0131n De\u011ferlendirilmesi:<\/strong>  E\u011fitim tamamland\u0131ktan sonra, modelin performans\u0131n\u0131 PSNR ve SSIM gibi metrikler kullanarak de\u011ferlendirin.  Ayr\u0131ca, g\u00f6rsel olarak da sonu\u00e7lar\u0131 inceleyin ve modelin ger\u00e7ek\u00e7i detaylar\u0131 ne kadar iyi yakalad\u0131\u011f\u0131n\u0131 g\u00f6zlemleyin.<\/p>\n
Ad\u0131m 4:  Uygulama:<\/strong>  E\u011fitilmi\u015f modeli, d\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc bir g\u00f6r\u00fcnt\u00fcy\u00fc y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc hale getirmek i\u00e7in kullan\u0131n.  Sonucu de\u011ferlendirin ve gerekirse modeli daha fazla e\u011fitin.<\/p>\n
Performans Optimizasyonu<\/strong><\/p>\n
SRGAN’\u0131n performans\u0131n\u0131 iyile\u015ftirmek i\u00e7in \u00e7e\u015fitli teknikler kullan\u0131labilir.  Bunlar aras\u0131nda:<\/p>\n
* Daha b\u00fcy\u00fck veri setleri kullan\u0131m\u0131:<\/strong>  Daha b\u00fcy\u00fck ve daha \u00e7e\u015fitli bir veri setiyle e\u011fitim yapmak, modelin genelleme yetene\u011fini art\u0131r\u0131r ve daha iyi sonu\u00e7lar elde edilmesini sa\u011flar.
\n* Model mimarisinin iyile\u015ftirilmesi:<\/strong>  Daha geli\u015fmi\u015f mimariler kullanmak, modelin \u00f6\u011frenme yetene\u011fini ve performans\u0131n\u0131 art\u0131rabilir.
\n* Hiperparametre optimizasyonu:<\/strong>  \u00d6\u011frenme oran\u0131, batch boyutu, kay\u0131p fonksiyonu gibi hiperparametreleri dikkatlice ayarlamak, modelin performans\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde etkiler.
\n* D\u00fczenleme teknikleri:<\/strong>  A\u015f\u0131r\u0131 \u00f6\u011frenmeyi \u00f6nlemek i\u00e7in dropout, weight decay gibi d\u00fczenleme teknikleri kullan\u0131labilir.
\n* Donan\u0131m iyile\u015ftirmeleri:<\/strong>  Daha g\u00fc\u00e7l\u00fc GPU’lar kullanmak, e\u011fitim s\u00fcresini k\u0131salt\u0131r ve daha b\u00fcy\u00fck ve karma\u015f\u0131k modellerin e\u011fitilmesini sa\u011flar.<\/p>\n
Ger\u00e7ek D\u00fcnya Vaka Analizleri<\/strong><\/p>\n
Vaka 1: Eski Foto\u011fraf Restorasyonu:<\/strong>  Aile alb\u00fcm\u00fcndeki eski, d\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc foto\u011fraflar\u0131 SRGAN ile restore edebilirsiniz.  Bu, eski an\u0131lar\u0131 daha net ve detayl\u0131 bir \u015fekilde yeniden ya\u015faman\u0131z\u0131 sa\u011flar.<\/p>\n
Vaka 2: G\u00fcvenlik Kameras\u0131 G\u00f6r\u00fcnt\u00fcleri:<\/strong>  G\u00fcvenlik kameralar\u0131ndan elde edilen d\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc g\u00f6r\u00fcnt\u00fcleri SRGAN ile iyile\u015ftirerek, olay yerindeki detaylar\u0131 daha net bir \u015fekilde g\u00f6rebilirsiniz.  Bu, su\u00e7lular\u0131n tan\u0131mlanmas\u0131nda veya kazalar\u0131n analizinde yard\u0131mc\u0131 olabilir.<\/p>\n
Vaka 3: T\u0131bbi G\u00f6r\u00fcnt\u00fcleme:<\/strong>  T\u0131bbi g\u00f6r\u00fcnt\u00fclerin \u00e7\u00f6z\u00fcn\u00fcrl\u00fc\u011f\u00fcn\u00fc art\u0131rarak, daha detayl\u0131 te\u015fhisler konulabilir ve daha do\u011fru tedavi planlar\u0131 olu\u015fturulabilir.  SRGAN, \u00f6zellikle d\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc MRI veya CT taramalar\u0131nda faydal\u0131 olabilir.<\/p>\n
Sonu\u00e7<\/strong><\/p>\n
SRGAN, d\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc g\u00f6r\u00fcnt\u00fcleri y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc hale getirmek i\u00e7in g\u00fc\u00e7l\u00fc bir y\u00f6ntemdir.  Ancak, modelin performans\u0131, veri setinin kalitesi, model mimarisi ve hiperparametrelere ba\u011fl\u0131d\u0131r.  Bu makalede, SRGAN’\u0131n temellerinden uygulamal\u0131 \u00f6rneklerine kadar geni\u015f bir yelpazede bilgi sunulmu\u015ftur.  Daha fazla bilgi i\u00e7in [Fatih Soysal’\u0131n web sitesini](https:\/\/fatihsoysal.com) ziyaret edebilirsiniz.<\/p>\n
S\u0131k\u00e7a Sorulan Sorular:<\/strong><\/p>\n
1. SRGAN’\u0131n di\u011fer s\u00fcper \u00e7\u00f6z\u00fcn\u00fcrl\u00fck y\u00f6ntemlerine g\u00f6re avantajlar\u0131 nelerdir?<\/strong>  SRGAN, \u00f6zellikle ger\u00e7ek\u00e7i detaylar\u0131n ve dokular\u0131n olu\u015fturulmas\u0131nda di\u011fer y\u00f6ntemlere g\u00f6re daha iyi performans g\u00f6sterir.<\/p>\n
2. SRGAN’\u0131 e\u011fitmek i\u00e7in ne kadar veriye ihtiyac\u0131m var?<\/strong>  Daha b\u00fcy\u00fck ve daha \u00e7e\u015fitli bir veri seti, daha iyi sonu\u00e7lar elde etmenizi sa\u011flar.  Ancak, minimum veri seti boyutu, kullan\u0131lan model mimarisine ve di\u011fer fakt\u00f6rlere ba\u011fl\u0131d\u0131r.<\/p>\n
3. SRGAN’\u0131n s\u0131n\u0131rlamalar\u0131 nelerdir?<\/strong>  SRGAN, \u00e7ok g\u00fcr\u00fclt\u00fcl\u00fc veya a\u015f\u0131r\u0131 bulan\u0131k g\u00f6r\u00fcnt\u00fclerde her zaman ba\u015far\u0131l\u0131 olmayabilir.  Ayr\u0131ca, e\u011fitim s\u00fcreci zaman alabilir ve hesaplama g\u00fcc\u00fc gerektirir.<\/p>\n
4. SRGAN’\u0131 hangi yaz\u0131l\u0131m ve donan\u0131m ile kullanabilirim?<\/strong>  SRGAN, TensorFlow veya PyTorch gibi derin \u00f6\u011frenme k\u00fct\u00fcphaneleri kullan\u0131larak uygulanabilir.  E\u011fitim s\u00fcreci i\u00e7in g\u00fc\u00e7l\u00fc bir GPU’ya ihtiya\u00e7 duyulabilir.<\/p>\n
5. SRGAN’\u0131 farkl\u0131 g\u00f6r\u00fcnt\u00fc t\u00fcrleri i\u00e7in nas\u0131l \u00f6zelle\u015ftirebilirim?<\/strong>  Belirli bir g\u00f6r\u00fcnt\u00fc t\u00fcr\u00fc i\u00e7in \u00f6zel olarak e\u011fitilmi\u015f bir SRGAN modeli geli\u015ftirebilirsiniz.  Bu, belirli bir t\u00fcr g\u00f6r\u00fcnt\u00fc i\u00e7in daha iyi performans elde etmenizi sa\u011flar.<\/p>\n
Yazar: Fatih Soysal<\/p>\n","protected":false},"excerpt":{"rendered":"# SRGAN ile G\u00f6r\u00fcnt\u00fc S\u00fcper \u00c7\u00f6z\u00fcn\u00fcrl\u00fc\u011f\u00fc: Ad\u0131m Ad\u0131m Uygulamal\u0131 K\u0131lavuz D\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc bir foto\u011fraf\u0131n\u0131z var ve onu olabildi\u011fince…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-29828","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nSRGAN: S\u00fcper \u00c7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc \u00dcretici \u00c7eki\u015fmeli A\u011flar<\/title>\n<meta name=\"description\" content=\"Hi\u00e7bir etiketi bulunamad\u0131.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/srgan-super-cozunurluklu-uretici-cekismeli-aglar\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SRGAN: S\u00fcper \u00c7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc \u00dcretici \u00c7eki\u015fmeli A\u011flar\" \/>\n<meta property=\"og:description\" content=\"Hi\u00e7bir etiketi bulunamad\u0131.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/srgan-super-cozunurluklu-uretici-cekismeli-aglar\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2025-09-20T07:40:22+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"8 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/srgan-super-cozunurluklu-uretici-cekismeli-aglar\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/srgan-super-cozunurluklu-uretici-cekismeli-aglar\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"SRGAN: S\u00fcper \u00c7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc \u00dcretici \u00c7eki\u015fmeli A\u011flar\",\"datePublished\":\"2025-09-20T07:40:22+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/srgan-super-cozunurluklu-uretici-cekismeli-aglar\/\"},\"wordCount\":1595,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/srgan-super-cozunurluklu-uretici-cekismeli-aglar\/#respond\"]}],\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/srgan-super-cozunurluklu-uretici-cekismeli-aglar\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/srgan-super-cozunurluklu-uretici-cekismeli-aglar\/\",\"name\":\"SRGAN: S\u00fcper \u00c7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc \u00dcretici \u00c7eki\u015fmeli A\u011flar\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2025-09-20T07:40:22+00:00\",\"description\":\"Hi\u00e7bir etiketi bulunamad\u0131.\",\"breadcrumb\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/srgan-super-cozunurluklu-uretici-cekismeli-aglar\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/srgan-super-cozunurluklu-uretici-cekismeli-aglar\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/srgan-super-cozunurluklu-uretici-cekismeli-aglar\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\/\/fatihsoysal.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"SRGAN: S\u00fcper \u00c7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc \u00dcretici \u00c7eki\u015fmeli A\u011flar\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/\",\"name\":\"Fatihsoysal.com\",\"description\":\"Blog - 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