IEEE Trans. on Computational Imaging

Official account for the IEEE Trans. on Computational Imaging journal.
Check out our spotlight talks:

[IEEE Trans. on Computational Imaging @IEEE_TCI]

Hello computational imaging friends and researchers 👋Follow this account for updates on newly published Trans. on Computational Imaging papers and news!

[IEEE Trans. on Computational Imaging @IEEE_TCI]

📣 "A Convergent Generalized Krylov Subspace Method for CS MRI Reconstruction with Gradient-Driven Denoisers" now on Early Access! https://t.co/aYfcQBCPyA
🗞️Reconst. with gradient-driven denoisers, achieving nonconvex convergence and fast reconst. for non-Cartesian acquisitions

[IEEE Trans. on Computational Imaging @IEEE_TCI]

📣📣"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)"by @sidgautam95,@MRInicole et al. Now on Early Access! https://t.co/Dt9oUA6wvj
🗞️A scan-adaptive MRI undersampling framework that selects optimized Cartesian masks per scan, outperforming fixed sampling

[IEEE Trans. on Computational Imaging @IEEE_TCI]

📣📣"Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in CS MRI Reconst"https://t.co/xKd33q94d6
🗞️A complex quasi-Newton proximal method for CS MRI reconst. that integrates gradient-driven denoisers with a Hermitian positive-definite Hessian estimate

[IEEE Trans. on Computational Imaging @IEEE_TCI]

📣📣"Using Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction" https://t.co/HRR1sErrLk
A method to compute/apply a randomized Nyström-based preconditioner on-the-fly using forward/adjoint operators, accelerating large-scale variational image reconst

[IEEE Trans. on Computational Imaging @IEEE_TCI]

📣📣 "Limitations of Data-Driven Spectral Reconstruction: An Optics-Aware Analysis" by @__QiangFu__ @EunsueChoi @SuhyunShin9812 @SeungHwanBaek8 et al. Now on Early Access! https://t.co/S2B0DT3qR4
🗞️ How metamerism and limited dataset diversity constrain RGB-to-spectral reconst.

[IEEE Trans. on Computational Imaging @IEEE_TCI]

🗞️Looking to accelerate compressed sensing MRI reconstruction? Check out
"Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction", which introduces a provable plug-and-play framework to achieve faster reconstruction.
https://t.co/TuI3ImjewN

[IEEE Trans. on Computational Imaging @IEEE_TCI]

📣📣"Fourier Analysis of Interference Scanning Optical Probe Microscopy" by @EmmanuelSoubies et al.
https://t.co/ASv0Vy5PfX
A Fourier analysis of Interf. Scanning Optical probe Microscopy (ISOM), offering new insights into this modality operating in the intermediate-field regime

[IEEE Trans. on Computational Imaging @IEEE_TCI]

📣📣"DAMMER: Direct Adaptive Multi-Resolution MEsh Reconstruction From X-Ray Measurements" by @imec_visionlab https://t.co/FsUYinkseV
DAMMER adaptively reconstructs attenuation values on a progressively optimized triangle mesh, diminishing partial volume effects.
@FWOVlaanderen

[IEEE Trans. on Computational Imaging @IEEE_TCI]

📣📣"Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction to ICF Applications"by @Xiaojian_Xu, @JasonHu07825282 et al: https://t.co/qcRZ0z18Y5
3D image reconst. using 2D convolutions with novel axis-swapping operations, avoiding volume slicing.

[IEEE Trans. on Computational Imaging @IEEE_TCI]

📣📣"Laser Protection via Jointly Learned Defocus and Image Reconstruction" by @jojomey87 et al. at @FraunhoferIOSB: https://t.co/d4Gqh5b0WI
A novel method protects optical sensors from laser damage by defocusing the imaging system and using an optimized CA and ML reconstruction.

[IEEE Trans. on Computational Imaging @IEEE_TCI]

🎉 Exciting News from the IEEE TCI! 📈
We are thrilled to announce that our journal's Impact Factor has increased to 4.8, as published in the 2025 Journal Citation Reports by Clarivate!.
🙏 A heartfelt thank you to our global community of contributors and readers.
#ThankYou

[IEEE Trans. on Computational Imaging @IEEE_TCI]

🚀 Stay updated with the latest in computational imaging!
Follow us on LinkedIn for news, paper highlights, and updates from IEEE Transactions on Computational Imaging.
🔗 Connect here: https://t.co/dYAYXovhZH
#ComputationalImaging #IEEE #TCI #Research

[IEEE Trans. on Computational Imaging @IEEE_TCI]

📢📢Multi-layered Surface Estimation for Low-cost Optical Coherence Tomography

If you're curious how you can implement a low-cost optical coherence tomography system don't miss this paper by J. Rapp et al. from @merl_news

Now on early access:
https://t.co/ZCwMh3lrlm

[IEEE Trans. on Computational Imaging @IEEE_TCI]

📽️📽️ New spotlight video for
"PRNet: Pyramid Restoration Network for RAW Image Super-Resolution" by Ling et al.

Check it out on our channel:
https://t.co/GBNzrRzJ0B

Paper:
https://t.co/ZL4rcgidxB

[IEEE Trans. on Computational Imaging @IEEE_TCI]

📢📢 Want to learn more about the @IEEE_TCI paper "A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI" by Hong, Hernandez-Garcia, and Fessler from @UMichCSE?

Check out the spotlight!
https://t.co/2DWPo9ngMi

Paper:
https://t.co/AnWS2Pazhg

[IEEE Trans. on Computational Imaging @IEEE_TCI]

Inverse Problems: Discrete or Continuous? @MehrsaPourya et al. from @big_epfl explain that box splines and multi-res bridge the gap:

"A Box-Spline Framework for Inverse Problems with Continuous-Domain Sparsity Constraints”
On early access:
https://t.co/DHIBwsHCKu

[IEEE Trans. on Computational Imaging @IEEE_TCI]

Got highly compressed and noisy radar data?

Plug-and-Play Regularization on Magnitude with Deep Priors for 3D Near-Field MIMO Imaging
by Oral (@okyanus_oral) and Oktem from the Middle East Technical University (@metu_eee)

https://t.co/JPnVFMSh6k

[IEEE Trans. on Computational Imaging @IEEE_TCI]

📣📣 "Local monotone operator learning using non-monotone operators: MnM-MOL", by M. John, J.R. Chand, and M. Jacob from @UIowaEngr now on early access!
https://t.co/moHTmiwYkY

For a sneak peek, watch this spotlight presentation:
https://t.co/1aB9wdAHUX

[IEEE Trans. on Computational Imaging @IEEE_TCI]

@stanley_h_chan Want to learn a little more? Here's the spotlight video on @IEEE_TCI's YouTube channel!
https://t.co/ISZibJY3Gi

[IEEE Trans. on Computational Imaging @IEEE_TCI]

If you want to learn the best kept secrets of non-blind Poisson deconvolution, check out our newest spotlight video:
https://t.co/ISZibJY3Gi
🤫🤫
Paper:
https://t.co/VlpfvEC0MA
by Gnanasambandam, Sanghvi and @stanley_h_chan from @PurdueECE.