Laser Range Data Denoising via Adaptive and Robust Dictionary Learning

Sparse representation (SR) is making significant impact in the computer vision and signal processing communities due to its stunning performance in a variety of applications for images, e.g., denoising, restoration, and synthesis. We propose an adaptive and robust SR algorithm that exploits the characteristics of typical laser range data, i.e., the availability of both range and reflectance data, to realize range data denoising. Specifically, our method estimates the informative level (IL) of each patch according to the variation in both range and reflectance modalities, followed by adaptive dictionary training that assigns dynamic sparsity weights to the patches with different ILs. Furthermore, the l₁-norm-based representation fidelity measure is applied to make our method robust to outliers, which are common in laser range measurements. Extensive experiments on synthesized and actual data demonstrate that our method works effectively, resulting in superior performance both visually and quantitatively.