Low-Light Image Enhancement With Semi-Decoupled Decomposition

You are here

IEEE Transactions on Multimedia

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.

Low-Light Image Enhancement With Semi-Decoupled Decomposition

Shijie Hao; Xu Han; Yanrong Guo; Xin Xu; Meng Wang

Low-light image enhancement is important for high-quality image display and other visual applications. However, it is a challenging task as the enhancement is expected to improve the visibility of an image while keeping its visual naturalness. Retinex-based methods have well been recognized as a representative technique for this task, but they still have the following limitations. First, due to less-effective image decomposition or strong imaging noise, various artifacts can still be brought into enhanced results. Second, although the priori information can be explored to partially solve the first issue, it requires to carefully model the priori by a regularization term and usually makes the optimization process complicated. In this paper, we address these issues by proposing a novel Retinex-based low-light image enhancement method, in which the Retinex image decomposition is achieved in an efficient semi-decoupled way. Specifically, the illumination layer I is gradually estimated only with the input image S based on the proposed Gaussian Total Variation model, while the reflectance layer R is jointly estimated by S and the intermediate I . In addition, the imaging noise can be simultaneously suppressed during the estimation of R . Experimental results on several public datasets demonstrate that our method produces images with both higher visibility and better visual quality, which outperforms the state-of-the-art low-light enhancement methods in terms of several objective and subjective evaluation metrics.

SPS on Twitter

  • Our 75th anniversary celebration continues -- March trivia is now live! Take the trivia before 6 April for the chan… https://t.co/5ajuy7OZkP
  • The Brain Space Initiative Talk Series continues on Friday, 31 March when Dr. Dean Salisbury presents "From searchi… https://t.co/jvnsJ6jX7K
  • On 26 April, join Dr. Preeti Kumari, Dr. Nitin Jonathan Myers, and Dr. Robert W. Heath Jr. for a new SPS Webinar, "… https://t.co/x5Mm6Djvmz
  • We have several 75th anniversary activities in store for - join SPS leaders in conversation about the p… https://t.co/nsPV0a5RW5
  • Join us on 7 April 2023 when Dr. Chao Zuo presents "Lens-Free On-Chip Digital Holographic Microscopy: Resolution An… https://t.co/Bklm6qy6zp

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar