Fusion of range and color images for denoising and resolution enhancement with a non-local filter
We present an integrated method for post-processing of range data which removes outliers, smoothes the depth values and enhances the lateral resolution in order to achieve visually pleasing 3D models from low-cost depth sensors with additional (registered) color images. The algorithm is based on the non-local principle and adapts the original NL-Means formulation to the characteristics of typical depth data. Explicitly handling outliers in the sensor data, our denoising approach achieves unbiased reconstructions from error-prone input data. Taking intra-patch similarity into account, we reconstruct strong discontinuities without disturbing artifacts and preserve fine detail structures, obtaining piece-wise smooth depth maps. Furthermore, we exploit the dependencies of the depth data with additionally available color information and increase the lateral resolution of the depth maps. We finally discuss how to parallelize the algorithm in order to achieve fast processing times that are adequate for post-processing of data from fast depth sensors such as time-of-flight cameras.
denoising nl-means time-of-flight outlier removal super-resolution