Multi-Stream Progressive Restoration for Low-Light Light Field Enhancement and Denoising

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.

Multi-Stream Progressive Restoration for Low-Light Light Field Enhancement and Denoising

By: 
Xianglang Wang; Youfang Lin; Shuo Zhang

Light Fields (LFs) are easily degraded by noise and low light. Low light LF enhancement and denoising are more challenging than single image tasks because the epipolar information among views should be taken into consideration. In this work, we propose a multiple stream progressive restoration network to restore the whole LF in just one forward pass. To make full use of the multiple views supplementary information and preserve the epipolar information, we design three types of input composed of view stacking. Each type of input corresponds to an restoration stream and provides specific complementary information. In addition, the weights are shared for each type of input in order to better maintain the epipolar information among views. To fully utilize the supplementary information, we then design a multi-stream interaction module to aggregate features from different restoration streams. Finally, the multiple stages restoration is introduced to reconstruct the LF progressively. We carry out extensive experiments to demonstrate that our model outperforms the state-of-the-art techniques on real world low light LF dataset and synthetic noisy LF dataset.

SPS Social Media

IEEE SPS Educational Resources

IEEE SPS Resource Center

IEEE SPS YouTube Channel