Abstract
Due to the rapid development of RGB-D sensors, increasing attention is being paid to depth image applications. Depth images play an important role in computer vision research. In this paper, we address the problem of inpainting for single depth images without corresponding color images as a guide. Within the framework of model-based optimization methods for depth image inpainting, the split Bregman iteration algorithm was used to transform depth image inpainting into the corresponding denoising subproblem. Then, we trained a set of efficient convolutional neural network (CNN) denoisers to solve this subproblem. Experimental results demonstrate the effectiveness of the proposed algorithm in comparison with three traditional methods in terms of visual quality and objective metrics.
Highlights
Depth images are a key research topic in the field of 2D-to-3D technology, which is widely used in machine vision and 3D reconstruction
Within the framework of model-based optimization methods, we investigated inpainting for single depth images
We adopt the split Bregman iteration (SBI) algorithm for the separation of the terms in Equation (1), and we train a set of effective convolutional neural network (CNN) denoisers and integrate the results into the framework of model-based optimization methods to complete the task of depth image inpainting
Summary
Depth images are a key research topic in the field of 2D-to-3D technology, which is widely used in machine vision and 3D reconstruction. Proposed an adaptive edge-oriented smoothing process to solve the depth image inpainting problem This method achieves a good balance between time consumption and image quality. With the help of variable splitting techniques, many scholars have decoupled the data-fidelity term and the regularization term in Equation (1) They have used various strategies to solve the resulting subproblems. We adopt the SBI algorithm for the separation of the terms in Equation (1), and we train a set of effective CNN denoisers and integrate the results into the framework of model-based optimization methods to complete the task of depth image inpainting. We trained a set of effective CNN denoisers that are used as important components of a model-based optimization method to solve the denoising subproblem.
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