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TÜREYEN, EZGİ DEMİRCAN

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TÜREYEN

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EZGİ DEMİRCAN

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    Adaptive Direction-Guided Structure Tensor Total Variation
    (Elsevier, 2021) Kamasak, Mustafa E.; TÜREYEN, EZGİ DEMİRCAN
    Direction-guided structure tensor total variation (DSTV) is a recently proposed regularization term that aims at increasing the sensitivity of the structure tensor total variation (STV) to the changes towards a predetermined direction. Despite of the plausible results obtained on the uni-directional images, the DSTV model is not applicable to the arbitrary (multi-directional and/or partly nondirectional) images. In this study, we build a two-stage denoising framework that brings adaptivity to the DSTV based denoising. We design a DSTV-like alternative to STV, which encodes the first-order information within a local neighborhood under the guidance of spatially varying directional descriptors (i.e., orientation and the dose of anisotropy). In order to estimate those descriptors, we propose an efficient preprocessor that captures the local geometry based on the structure tensor. Through the extensive experiments, we demonstrate how beneficial the involvement of the directional information in STV is, by comparing the proposed method with the state-of-the-art analysis-based denoising models, both in terms of quality and computational efficiency.
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    Restoring Fluorescence Microscopy Images by Transfer Learning From Tailored Data
    (IEEE-Inst Electrical Electronics Engineers Inc., 2022) AKBULUT, FATMA PATLAR; TÜREYEN, EZGİ DEMİRCAN; Kamasak, Mustafa E.
    In fluorescence microscopy imaging, noise is a very usual phenomenon. To some extent, it can be suppressed by increasing the amount of the photon exposure; however, it is not preferable since this may not be tolerated by the subjected specimen. Thus, a sophisticated computational method is needed to denoise each acquired micrograph, so that they become more adequate for further feature extraction and image analysis. However, apart from the difficulties of the denoising problem itself, one main challenge is that the absence of the ground-truth images makes the data-driven techniques less applicable. In order to tackle this challenge, we suggest to tailor a dataset by handpicking images from unrelated source datasets. Our tailoring strategy involves exploring some low-level view-based features of the candidate images, and their similarities to those of the fluorescence microscopy images. We pretrain and fine-tune the well-known feed-forward denoising convolutional neural networks (DnCNNs) on our tailored dataset and a very limited amount of fluorescence images, respectively to ensure both the diversity and the content-awareness. The quantitative and visual experimentation show that our approach is able to curate a dataset, which is significantly superior to the arbitrarily chosen source images, and well-approximates to the fluorescence images. Moreover, the combination of the tailored dataset with a few fluorescence data through the use of fine-tuning offers a good balance between the generalization capability and the content-awareness, on the majority of considered scenarios.
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    Publication
    Nonlocal Adaptive Direction-Guided Structure Tensor Total Variation for Image Recovery
    (Springer London Ltd., 2021) TÜREYEN, EZGİ DEMİRCAN; Kamasak, Mustafa E.
    A common strategy in variational image recovery is utilizing the nonlocal self-similarity property, when designing energy functionals. One such contribution is nonlocal structure tensor total variation (NLSTV), which lies at the core of this study. This paper is concerned with boosting the NLSTV regularization term through the use of directional priors. More specifically, NLSTV is leveraged so that, at each image point, it gains more sensitivity in the direction that is presumed to have the minimum local variation. The actual difficulty here is capturing this directional information from the corrupted image. In this regard, we propose a method that employs anisotropic Gaussian kernels to estimate directional features to be later used by our proposed model. The experiments validate that our entire two-stage framework achieves better results than the NLSTV model and two other competing local models, in terms of visual and quantitative evaluation.