Depth Super-Resolution via Joint Color-Guided Internal and External Regularizations


Many real-world applications make heavy use of depth information. In practise, however, depth maps tend to have a lower resolution than colour images because of the limitations of depth sensor technologies. To achieve depth super-resolution, we propose to combine the internal smoothness prior and the exterior gradient consistency requirement in graph domain. Firstly, a novel graph Laplacian regularizer is presented to preserve the depth's inherent piecewise smoothness, which has useful filtering qualities. The weight matrix of the respect graph has been defined to take advantage of both depth and the related guidance image. In contrast, we introduce a graph gradient consistency constraint to guarantee that the graph gradient of depth is near to the thresholded gradient of guidance, inspired by the finding that the gradient of depth is modest except at edge separation regions. The gradient thresholding model is reinterpreted as variational optimization with a sparsity constraint. Here, we address the issue of structure inconsistency between the depth and guidance. Finally, a unified optimization framework incorporating both internal and external regularizations may be addressed effectively by ADMM. Experiments show that our strategy outperforms the current state of the art in terms of both objective and subjective quality evaluations.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Joint Transceiver Beamforming Design for Hybrid Full-Duplex and Half-Duplex Ad-Hoc Networks ABSTRACT: In this paper, we propose a joint transceiver beamforming design method for hybrid full-duplex (FD) and half-duplex
PROJECT TITLE : Joint Optimization of MapReduce Scheduling and Network Policy in Hierarchical Data Centers ABSTRACT: The use of mapreduce frameworks to analyze ever-increasing volumes of data is expected to continue increasing
PROJECT TITLE : Joint Computation Offloading and Bandwidth Assignment in Cloud-Assisted Edge Computing ABSTRACT: The process of augmenting the computational capabilities of mobile devices with limited resources by offloading computation
PROJECT TITLE : Message-Passing-Based Joint User Association and Time Allocation for Wireless Powered Communication Networks ABSTRACT: A joint design of user association and time allocation for wirelessly powered communication
PROJECT TITLE : Joint detection and matching of feature points in multimodal images ABSTRACT: In this work, we propose a novel architecture for Convolutional Neural Networks (CNNs) for the joint detection and matching of feature

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry