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    Reducing Speckle Noise from Ultrasound Images Using an Autoencoder Network
    (Ieee, 2020) Karaoglu, Onur; Bilge, Hasan Sakir; Uluer, Ihsan
    Image enhancement aims to obtain a clear image from a noisy image and it also uses for ultrasound images. In the experimental study, unlike classical image enhancement methods, deep learning method was used. Different levels of speckle noise added to the ultrasound images of the brachial plexus, which is known as the large nerve community under the armpit, were tried to be removed with the help of the convolutional denoising autoencoder network, which is one of the deep learning methods. The results obtained from the experimental study were compared with classical methods results and the proposed method was found to be more successful than classical methods.
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    Removal of speckle noises from ultrasound images using five different deep learning networks
    (Elsevier - Division Reed Elsevier India Pvt Ltd, 2022) Karaoglu, Onur; Bilge, Hasan Sakir; Uluer, Ihsan
    Image enhancement methods are applied to medical images to reduce the noise that they contain. There are many academic studies in the literature using classical image enhancement methods. Ultrasound imaging is a medical imaging method that is used for the diagnosis of diseases. In this study, speckle noises with Rayleigh distribution at four different noise levels (sigma = 0.10, 0.25, 0.50, 0.75) are added to ultrasound images of the brachial plexus nerve region. Five different deep learning networks (Dilated Convolution Autoencoder Denoising Network/Di-Conv-AE-Net, Denoising U-Shaped Net/D-U-Net, BatchRenormalization U-Net/Br-U-Net, Generative Adversarial Denoising Network/DGan-Net, and CNN Residual Network/DeRNet) are used for reducing the speckle noises of the ultrasound images. The performances of the deep networks are compared with block-matching and 3D filtering (BM3D), which is one of the most preferred classical image enhancement algorithms; with classical filters including Bilateral, Frost, Kuan, Lee, Mean, and Median Filters; and with deep learning networks including Learning Pixel Distribution Prior with Wider Convolution for Image Denoising (WIN5-RB), Denoising Prior Driven Deep Neural Network for Image Restoration (DPDNN), and Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks (FPD-M-Net). Network performance is evaluated according to peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and runtime criteria and the proposed deep learning networks are shown to outperform the other networks. (C) 2021 Karabuk University. Publishing services by Elsevier B.V.