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IEEE Transactions on Computational Imaging is a peer-reviewed journal published by IEEE, covering Medical Imaging Techniques and Applications, Sparse and Compressive Sensing Techniques, Advanced Image Processing Techniques. Over its lifetime it has published 1,167 papers, which have received 24,614 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Computational Imaging, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Computational Imaging style. No sign-up. |
| Abbreviation (ISO-4) | TCI |
| Publishes research in | Medical Imaging Techniques and Applications Sparse and Compressive Sensing Techniques Advanced Image Processing Techniques Image and Signal Denoising Methods Photoacoustic and Ultrasonic Imaging |
| ISSN | 2333-9403 |
| Citation impact (2-yr) | 3.71 |
| h-index | 69 |
| i10-index | 465 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | ieeexplore.ieee.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention in the context of image processing: the default and virtually only choice is ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML"…
Convolutional neural networks (CNN) are a special type of deep neural networks (DNN). They have so far been successfully applied to image super-resolution (SR) as well as other image restoration tasks. In this paper, we consider the problem of video super-resolution. We propose a CNN that is trained on both the spatial and the temporal…
Many material and biological samples in scientific imaging are characterized by nonlocal repeating structures. These are studied using scanning electron microscopy and electron tomography. Sparse sampling of individual pixels in a two-dimensional image acquisition geometry, or sparse sampling of projection images with large tilt increments in a tomography experiment, can enable high speed data acquisition…
Existing image fusion methods always use hand-crafted fusion rules due to the uninterpretability of deep feature maps, which restrict the performance of networks and result in distortion. To address these limitations, this paper for the first time realizes the interpretable importance evaluation of feature maps in a deep learning manner. This importance-oriented fusion rule helps…
Given an image, we wish to produce an image of larger size with significantly more pixels and higher image quality. This is generally known as the single image super-resolution problem. The idea is that with sufficient training data (corresponding pairs of low and high resolution images) we can learn set of filters (i.e., a mapping)…
