Derin Öğrenme ile Görüntülerde Gürültü Azaltma Üzerine Kapsamlı Bir İnceleme

Abstract

Günlük hayatımızda ve bilimsel araştırmalarda gerçeğe yakın ve gürültüsüz görüntülere olan ihtiyaç artmaktadır. Ancak görüntüler, gürültü ile bozulmakta ve bu da görsel görüntü kalitesinin düşmesine neden olmaktadır. Bu nedenle, görüntü özelliklerini kaybetmeden gürültüyü azaltmak için çalışmalar yapılmaktadır. Şimdiye kadar, gürültüyü azaltmak için çeşitli yöntemler önerilmiş olup, her yöntemin farklı avantajları bulunmaktadır. Bu makalede, alanında en iyi sonucu elde eden yöntemler hakkında bilgi verilerek, video ve sabit görüntülerinde gürültü azaltma alanında yapılan geleneksel gürültü giderme yöntemleri ve derin öğrenme yöntemlerine ait çalışmalar özetlenip, elde edilen sonuçlar birbirleriyle karşılaştırılmaktadır. Yapılan araştırmalar deneylerin toplamsal beyaz Gauss gürültüsü durumuna odaklandığını göstermektedir. Görüntülerde gürültü giderme aşamasında zaman içerisinde geleneksel gürültü giderme yöntemleri, makine öğrenmesi yöntemleri, derin öğrenme yöntemleri ve diğer matematiksel yöntemler kullanılmış olup, derin öğrenme yöntemleri daha başarılı sonuçlar elde etmektedir. Ancak elde edilen verilere göre orijinal görüntü çiftlerine sahip olmadan modelin eğitilmesi konusunda çalışmaların yetersiz olduğu ve değişik gürültü seviyelerinde tek bir yöntemin başarılı olamadığı görülmüştür. İleride yapılacak çalışmalarda gerçek hayattaki görüntülerde var olan gürültülerin nasıl giderileceği konusuna daha detaylı odaklanılması gerektiği görülmektedir.

Keywords

• Görüntülerde Gürültü Giderme
• Görüntü restorasyonu
• Video üzerinde gürültü azaltma
• Derin öğrenme tabanlı gürültü giderme

A Comprehensive Review of Image Denoising With Deep Learning

Abstract

In daily life and scientific searches, the need for real-like and denoised images is increasing. But images are distorted by noise, resulting in lower visual image quality. For this reason, noise removal studies are carried out on images to increase the quality. Until now, various methods have been proposed to decrease noise, each technique has different advantages. This paper gives information about the methods that achieve the best results in their field and summarizes the studies about traditional denoising and deep learning based denoising methods in the field of noise reduction in video and images and compares the studies with each other. Researches show that experiments focus on the case of additive white Gaussian noise. Traditional noise removal methods, machine learning methods, deep learning methods and other mathematical methods have been used in image denoising problem over time, and deep learning methods achieve more successful results. However, according to the obtained data, it has been seen that the studies on training the model without having the original image pairs were insufficient and a single method could not be successful at different noise levels. In future studies, it is necessary to focus on how to remove the noise in real-life images.

Keywords

• Image denoising
• Image restoration
• Video denoising
• Deep learning-based noise reduction

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