IMAGE DENOISING USING DEEP CONVOLUTIONAL AUTOENCODERS
Location: AB1 412
Asst. Prof. Furkan Kıraç, Özyeğin University
Asst. Prof. Reyhan Aydoğan, Özyeğin University
Prof. Lale Akarun, BoğaziçiUniversity
Image denoising is one of the fundamental problems in image processing eld since it is required by many computer vision applications. Various approaches have been used in image denoising throughout the years from spatial filtering to model based approaches. Having outperformed all traditional methods, neural network-based discriminative methods have gained popularity in recent years. However, most of these methods still struggle to achieve flexibility against various noise levels and types. In this thesis, we propose a deep convolutional autoencoder combined with a variant of feature pyramid network for image denoising. We use simulated data in Blender software along with corrupted natural images during training to improve robustness against various noise levels and types. Our experimental results show that the proposed method can achieve competitive performance in blind Gaussian denoising with significantly less training time required compared to state-of-the-art methods. Extensive experiments showed us the proposed method gives a promising performance in a wide range of noise levels with a single network.
Ekrem Çetinkaya received his B.Sc. degree in computer science from Ozyegin University in 2018. Following his graduation, he started pursuing his M.Sc. degree in Ozyegin University under the supervision of Professor M. Furkan Kıraç on image processing with deep learning. His research interests include Deep Learning, Computer Vision and Multimedia Networking.
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