IEEE Transactions on Computational Imaging

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Scope

The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.

Reproducible research

The Transactions encourages authors to make their publications reproducible by making all information needed to reproduce the presented results available online. This typically requires publishing the code and data used to produce the publication's figures and tables on a website; see the supplemental materials section of the information for authors. It gives other researchers easier access to the work, and facilitates fair comparisons.

Multimedia content

It is now possible to submit for review and publish in Xplore supporting multimedia material such as speech samples, images, movies, matlab code etc. A multimedia graphical abstract can also be displayed along with the traditional text. More information is available under Multimedia Materials at the IEEE Author Digital Toolbox.

Call for papers

Special Section on Computational Imaging using Synthetic Apertures: The IEEE Transactions on Computational Imaging (TCI) invites submissions for a Special Section on Computational Imaging using Synthetic Apertures.

 

TCI Volume 10 | 2024

Annealed Score-Based Diffusion Model for MR Motion Artifact Reduction

TCI Volume 10 | 2024
TCI Articles

Motion artifact reduction is one of the important research topics in MR imaging, as the motion artifact degrades image quality and makes diagnosis difficult. Recently, many deep learning approaches have been studied for motion artifact reduction. Unfortunately, most existing models are trained in a supervised manner, requiring paired motion-corrupted and motion-free images, or are based on a strict motion-corruption model, which limits their use for real-world situations.

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A 3-D Ultrasound Tomography Method for Bone Morphology Evaluation

TCI Volume 10 | 2024
TCI Articles

Accurately imaging bones using ultrasound has been a long-standing challenge, primarily due to the high attenuation, significant acoustic impedance contrast at cortical boundaries, and the unknown distribution of sound velocity. Furthermore, two-dimensional (2-D) ultrasound bone imaging has limitations in diagnosing osteoporosis from a morphological perspective, as it lacks stereoscopic spatial information.

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Simultaneous Multifrequency Demodulation for Single-Shot Multiple-Path ToF Imaging

TCI Volume 10 | 2024
TCI Articles

Indirect Time-of-Flight (iToF) sensors measure the received signal's phase shift or time delay to calculate depth. In realistic conditions, however, recovering depth is challenging as reflections from secondary scattering areas or translucent objects may interfere with the direct reflection, resulting in inaccurate 3D estimates. 

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Differentiable Uncalibrated Imaging

TCI Volume 10 | 2024
TCI Articles

We propose a differentiable imaging framework to address uncertainty in measurement coordinates such as sensor locations and projection angles. We formulate the problem as measurement interpolation at unknown nodes supervised through the forward operator. To solve it we apply implicit neural networks, also known as neural fields, which are naturally differentiable with respect to the input coordinates. We also develop differentiable spline interpolators which perform as well as neural networks, require less time to optimize and have well-understood properties.

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