Electrical & Electronics Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10679/44

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Browsing by Institution Author "ATEŞ, Hasan Fehmi"

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    Deep learning based event recognition in aerial imagery
    (IEEE, 2023) Şahin, A. H.; Ateş, Hasan Fehmi; Electrical & Electronics Engineering; ATEŞ, Hasan Fehmi
    In this paper, we investigate event recognition for aerial surveillance. This is a significant task especially when we consider the growing popularity of UAVs. The main purpose of the paper is to detect events both at the clip level in aerial videos and also at the frame level in aerial images. To achieve this goal, novel deep learning models and training techniques are used. In this work, we propose new model architectures to detect events in both image and video domains. The developed models are tested on the ERA dataset. Results show that the proposed models achieve state-of-the-art performance on both single images and aerial video clips of the ERA dataset.
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    Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimation
    (Elsevier, 2023-08) Ateş, Hasan Fehmi; Yildirim, S.; Gunturk, B. K.; Electrical & Electronics Engineering; ATEŞ, Hasan Fehmi
    Blind single image super-resolution (SISR) is a challenging task in image processing due to the ill-posed nature of the inverse problem. Complex degradations present in real life images make it difficult to solve this problem using naïve deep learning approaches, where models are often trained on synthetically generated image pairs. Most of the effort so far has been focused on solving the inverse problem under some constraints, such as for a limited space of blur kernels and/or assuming noise-free input images. Yet, there is a gap in the literature to provide a well-generalized deep learning-based solution that performs well on images with unknown and highly complex degradations. In this paper, we propose IKR-Net (Iterative Kernel Reconstruction Network) for blind SISR. In the proposed approach, kernel and noise estimation and high-resolution image reconstruction are carried out iteratively using dedicated deep models. The iterative refinement provides significant improvement in both the reconstructed image and the estimated blur kernel even for noisy inputs. IKR-Net provides a generalized solution that can handle any type of blur and level of noise in the input low-resolution image. IKR-Net achieves state-of-the-art results in blind SISR, especially for noisy images with motion blur.