Abstract
The depth images acquired by consumer depth sensors
(e.g., Kinect and ToF) usually are of low resolution and insufficient quality. One natural solution is to incorporate
with high resolution RGB camera for exploiting their statistical correlation. However, most existing methods are intuitive and limited in characterizing the complex and dynamic dependency between intensity and depth images. To
address these limitations, we propose a weighted analysis
representation model for guided depth image enhancement,
which advances the conventional methods in two aspects:
(i) task driven learning and (ii) dynamic guidance. First,
we generalize the analysis representation model by including a guided weight function for dependency modeling. The
task-driven learning formulation is introduced to obtain the
optimized guidance tailored to specific enhancement tasks.
Second, the depth image is gradually enhanced along with
the iterations, and thus the guidance should also be dynamically adjusted to account for the updating of depth image.
To this end, stage-wise parameters are learned for dynamic
guidance. Experiments on guided depth image upsampling
and noisy depth image restoration validate the effectiveness
of our method.