JSTSP Volume 14 Issue 6

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

2021

JSTSP Volume 14 Issue 6

In magnetic resonance imaging (MRI), several images can be obtained using different imaging settings (e.g. T1, T2, DWI, and Flair). These images have similar anatomical structures but are with different contrasts, which provide a wealth of information for diagnosis.

Recently, deep neural network (DNN) based methods for low-dose CT have been investigated to achieve excellent performance in both image quality and computational speed. However, almost all methods using DNNs for low-dose CT require clean ground truth data with full radiation dose to train the DNNs. In this work, we attempt to train DNNs for low-dose CT reconstructions with reduced tube current by investigating unsupervised training of DNNs for denoising sensor measurements or sinograms without full-dose ground truth images.

Regularization by denoising (RED) is an image reconstruction framework that uses an image denoiser as a prior. Recent work has shown the state-of-the-art performance of RED with learned denoisers corresponding to pre-trained convolutional neural nets (CNNs). In this work, we propose to broaden the current denoiser-centric view of RED by considering priors corresponding to networks trained for more general artifact-removal.

SPS on Twitter

  • Students, it's time to form your teams! The 2022 Signal Processing Cup competition is underway. This year's topic,… https://t.co/fVw7tA7zTG
  • The DEGAS Webinar Series continues this Thursday, 13 January when Peter Battaglia presents "Modeling Physical Struc… https://t.co/Kndvzl8BpE
  • The SPS Webinar Series continues on Wednesday, 26 January when Dr. Ba-Ngu Vo presents "Bayesian Multi-object Tracki… https://t.co/sKejcUeyys
  • Call for Speakers! The IEEE Women in Engineering International Leadership Conference () is seeking submissio… https://t.co/bSaRgMw2uD
  • CALL FOR PROPOSALS: The SPS Education Board is soliciting proposals for short courses in conjunction with ICASSP 20… https://t.co/LiVjYgEVGU

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar