Time-of-Flight (ToF) depth sensing camera is able to obtain depth maps at a high frame rate. However, its low resolution and sensitivity to the noise are always a concern. A popular solution is upsampling the obtained noisy low resolution depth map with the guidance of the companion high resolution color image. However, due to the constrains in the existing upsampling models, the high resolution depth map obtained in such way may suffer from either texture copy artifacts or blur of depth discontinuity. In this paper, a novel optimization framework is proposed with the brand new data term and smoothness term. The comprehensive experiments using both synthetic data and real data show that the proposed method well tackles the problem of texture copy artifacts and blur of depth discontinuity. It also demonstrates sufficient robustness to the noise. Moreover, a data driven scheme is proposed to adaptively estimate the parameter in the upsampling optimization framework. The encouraging performance is maintained even in the case of large upsampling e.g. @math and @math .
One of the most well known image guided upsampling methods is based on the Markov Random Fields (MRF) @cite_12 . It is further extended in @cite_4 which we denote as NLM-MRF. Both methods along with other similar methods such as @cite_8 @cite_7 @cite_18 @cite_19 @cite_17 @cite_1 @cite_21 which adopt the certain cues on the corresponding available color image as the reference to upsample the low resolution depth map. They all assume that the color edge co-occurs with the depth discontinuity. To clearly demonstrate the essential constrains of the existing methods, we brief the existing modeling as follows.
@cite_18 @cite_4 @cite_7 @cite_8 @cite_21 @cite_1 @cite_19 @cite_12 @cite_17
15713842 2162366888 2130248603 2275363859
In this paper, we propose to conduct inpainting and upsampling for defective depth maps when aligned color images are given. These tasks are referred to as the guided depth enhancement problem. We formulate the problem based on the heat diffusion framework. The pixels with known depth values are treated as the heat sources and the depth enhancement is performed via diffusing the depth from these sources to unknown regions. The diffusion conductivity is designed in terms of the guidance color image so that a linear anisotropic diffusion problem is formed. We further cast the steady state problem of this diffusion into the famous random walk model, by which the enhancement is achieved efficiently by solving a sparse linear system. The proposed algorithm is quantitatively evaluated on the Middlebury stereo dataset and is applied to inpaint Kinect data and upsample Lidar's range data. Comparisons to the commonly used bilateral filter and Markov Random Field based methods are also presented, showing that our algorithm is competent. Markov Random Fields (MRFs) are ubiquitous in low- level computer vision. In this paper, we propose a new approach to the optimization of multi-labeled MRFs. Similarly to a-expansion it is based on iterative application of binary graph cut. However, the number of binary graph cuts required to compute a labelling grows only logarithmically with the size of label space, instead of linearly. We demonstrate that for applications such as optical flow, image restoration, and high resolution stereo, this gives an order of magnitude speed-up, for comparable energies. Iterations are performed by "fusion" of solutions, done with QPBO which is related to graph cut but can deal with non- submodularity. At convergence, the method achieves optima on a par with the best competitors, and sometimes even exceeds them. We present a new approach for the discriminative training of continuous-valued Markov Random Field (MRF) model parameters. In our approach we train the MRF model by optimizing the parameters so that the minimum energy solution of the model is as similar as possible to the ground-truth. This leads to parameters which are directly optimized to increase the quality of the MAP estimates during inference. Our proposed technique allows us to develop a framework that is flexible and intuitively easy to understand and implement, which makes it an attractive alternative to learn the parameters of a continuous-valued MRF model. We demonstrate the effectiveness of our technique by applying it to the problems of image denoising and in-painting using the Field of Experts model. In our experiments, the performance of our system compares favourably to the Field of Experts model trained using contrastive divergence when applied to the denoising and in-painting tasks. This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images. The generative MRF acts on higher-levels of a dCNN feature pyramid, controling the image layout at an abstract level. We apply the method to both photographic and non-photo-realistic (artwork) synthesis tasks. The MRF regularizer prevents over-excitation artifacts and reduces implausible feature mixtures common to previous dCNN inversion approaches, permitting synthezing photographic content with increased visual plausibility. Unlike standard MRF-based texture synthesis, the combined system can both match and adapt local features with considerable variability, yielding results far out of reach of classic generative MRF methods.