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Non-line-of-sight reconstruction via structure sparsity regularization
Optics Letters
|September 14, 2023
Summary
This study introduces structure sparsity (SS) regularization to improve non-line-of-sight (NLOS) imaging reconstruction quality. The novel method enhances denoising, enabling clearer imaging of occluded objects even with low signal-to-noise ratio (SNR) data.
Area of Science:
- Optics and Photonics
- Computer Vision
- Signal Processing
Background:
- Non-line-of-sight (NLOS) imaging enables seeing around corners, crucial for autonomous driving and security.
- Reconstruction quality in NLOS imaging is often degraded by low signal-to-noise ratio (SNR) measurements.
Purpose of the Study:
- To develop a novel regularization method for denoising in NLOS reconstruction.
- To improve the quality of reconstructed images from low-SNR NLOS measurements.
Main Methods:
- Introduced structure sparsity (SS) regularization, incorporating nuclear norm penalization into the directional light-cone transform (DLCT) model.
- Optimized a directional albedo model with SS regularization using the fast iterative shrinkage-thresholding algorithm (FISTA).
Main Results:
- Achieved robust reconstruction of occluded objects.
- Demonstrated high-quality reconstructions surpassing state-of-the-art methods, particularly in short exposure and low-SNR conditions.
- Validated through comprehensive evaluations on synthetic and experimental datasets.
Conclusions:
- Structure sparsity regularization effectively denoises NLOS reconstruction by leveraging directional albedo neighborhood information.
- The proposed method significantly enhances NLOS imaging capabilities, especially in challenging low-SNR environments.
