Deep Recursive Network for Hyperspectral Image Super-Resolution

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.

Deep Recursive Network for Hyperspectral Image Super-Resolution

Wei Wei; Jiangtao Nie; Yong Li; Lei Zhang; Yanning Zhang

Fusion based hyperspectral image (HSI) super-resolution method, which obtains a spatially high-resolution (HR) HSI by fusing a low-resolution (LR) HSI and an HR conventional image, has been a prevalent method for HSI super-resolution. One effective fusion based method is to cast HSI super-resolution into a unified optimization problem, where handcrafted priors such as sparse prior or low rank prior are always adopted to regularize the latent HR HSI to be optimized. However, these priors show limitations in generalizing to challenging cases due to the heuristic assumption on image statistics as well as the restricted expressiveness capacity of the shallow structure. Taking advantages of the powerful expression ability of deep learning based method, a new HSI super-resolution network is proposed which implicitly incorporates a deep structure as the regularizer/prior. Specifically, we reformulate the original unified optimization problem into three sub-optimization problems, one is related with the regularizer and the others are without. Thanks to the fact that the one related with the regularizer naturally equals to a denoising problem, a recursive residual network is proposed for this sub-optimization problem. In addition, we unfold the other sub-optimization problems into network representations, with which the original unified optimization problem can be represented into a fully end-to-end network. Experimental results shows the superiority of the proposed method for HSI super-resolution on three benchmark datasets.

SPS on Twitter

  • NEW SPS WEBINAR: On Tuesday, 13 December, join Dr. Qian Huang for "Deep Learning for All-in-Focus Imaging" - regist…
  • Join the SPS Membership Drive on Monday, 12 December, when SPS members, potential members, and the greater signal p…
  • The fundraising deadline to meet our 30 unique donations of US$10 or more is tonight — increase your impact for sig…
  • Happy ! Celebrate this global day of generosity and community action with the IEEE Foundation and…
  • The SPS Biomedical Imaging and Signal Processing Technical Committee Webinar Series continues on Tuesday, 6 Decembe…

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar