Depth Super-Resolution by Transduction
Further Details Contact: A Vinay 9030333433, 08772261612
Email: takeoffstudentprojects@gmail.com | www.takeoffprojects.com
Abstract
This paper presents a depth super-resolution (SR) method that uses both of a low-resolution (LR)
depth image and a high-resolution (HR) intensity image. We formulate depth SR as a graph-based
transduction problem. Specifically, the HR intensity image is represented as an undirected graph,
in which pixels are characterized as vertices, and their relations are encoded as an affinity function.
When the vertices initially labeled with certain depth hypotheses (from the LR depth image) are
regarded as input queries, all the vertices are scored with respect to the relevance’s to these queries
by a classifying function. Each vertex is then labeled with the depth hypothesis that receives the
highest relevance score. We design the classifying function by taking into account the local and
global structures of the HR intensity image. This approach enables us to address a depth bleeding
problem that typically appears in current depth SR methods. Furthermore, input queries are
assigned in a probabilistic manner, making depth SR robust to noisy depth measurements. We also
analyze existing depth SR methods in the context of transduction, and discuss their theoretic
relations. Intensive experiments demonstrate the superiority of the proposed method over state-ofthe-art methods both qualitatively and quantitatively.
Existing Method:
An actual application has been impeded by inherent physical limitations of the sensor – the
acquired depth image is of relatively low-resolution (LR), and is corrupted by a huge amount of
noise.
Demerits
High complexity
Proposed Method
We design the classifying function by taking into account the local and global structures of the HR
intensity image.
Merits:
Execution time is less
Further Details Contact: A Vinay 9030333433, 08772261612
Email: takeoffstudentprojects@gmail.com | www.takeoffprojects.com
Further Details Contact: A Vinay 9030333433, 08772261612
Email: takeoffstudentprojects@gmail.com | www.takeoffprojects.com