We report a method for super-resolution of range images. Our approach leverages the interpretation of LR image as sparse samples on the HR grid. Based on this interpretation, we demonstrate that our recently reported approach, which reconstructs dense range images from sparse range data by exploiting a registered colour image, can be applied for the task of resolution enhancement of range images. Our method only uses a single colour image in addition to the range observation in the super-resolution process. Using the proposed approach, we demonstrate super-resolution results for large factors (e.g. 4) with good localization accuracy.
As mentioned earlier, the idea of using a colour image has been explored for range super-resolution in various ways. For example, the work in @cite_0 interpolates the range image and, by exploiting the assumption that depth discontinuities coincide with color edges, improves estimation at discontinuities. Similar improvements are shown in MRF-based energy minimization approaches that also uses a HR optical image @cite_8 @cite_3 . However, these approaches are not known to perform for large super-resolution factors.
@cite_0 @cite_3 @cite_8
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Recent research on super-resolution has achieved great success due to the development of deep convolutional neural networks (DCNNs). However, super-resolution of arbitrary scale factor has been ignored for a long time. Most previous researchers regard super-resolution of different scale factors as independent tasks. They train a specific model for each scale factor which is inefficient in computing, and prior work only take the super-resolution of several integer scale factors into consideration. In this work, we propose a novel method called Meta-SR to firstly solve super-resolution of arbitrary scale factor (including non-integer scale factors) with a single model. In our Meta-SR, the Meta-Upscale Module is proposed to replace the traditional upscale module. For arbitrary scale factor, the Meta-Upscale Module dynamically predicts the weights of the upscale filters by taking the scale factor as input and use these weights to generate the HR image of arbitrary size. For any low-resolution image, our Meta-SR can continuously zoom in it with arbitrary scale factor by only using a single model. We evaluated the proposed method through extensive experiments on widely used benchmark datasets on single image super-resolution. The experimental results show the superiority of our Meta-Upscale. This paper proposes a new visual SLAM technique that not only integrates 6 degrees of freedom (DOF) pose and dense structure but also simultaneously integrates the colour information contained in the images over time. This involves developing an inverse model for creating a super-resolution map from many low resolution images. Contrary to classic super-resolution techniques, this is achieved here by taking into account full 3D translation and rotation within a dense localisation and mapping framework. This not only allows to take into account the full range of image deformations but also allows to propose a novel criteria for combining the low resolution images together based on the difference in resolution between different images in 6D space. Another originality of the proposed approach with respect to the current state of the art lies in the minimisation of both colour (RGB) and depth (D) errors, whilst competing approaches only minimise geometry. Several results are given showing that this technique runs in real-time (30Hz) and is able to map large scale environments in high-resolution whilst simultaneously improving the accuracy and robustness of the tracking. Depth image super-resolution is an extremely challenging task due to the information loss in sub-sampling. Deep convolutional neural network has been widely applied to color image super-resolution. Quite surprisingly, this success has not been matched to depth super-resolution. This is mainly due to the inherent difference between color and depth images. In this paper, we bridge up the gap and extend the success of deep convolutional neural network to depth super-resolution. The proposed deep depth super-resolution method learns the mapping from a low-resolution depth image to a high-resolution one in an end-to-end style. Furthermore, to better regularize the learned depth map, we propose to exploit the depth field statistics and the local correlation between depth image and color image. These priors are integrated in an energy minimization formulation, where the deep neural network learns the unary term, the depth field statistics works as global model constraint and the color-depth correlation is utilized to enforce the local structure in depth image. Extensive experiments on various depth super-resolution benchmark datasets show that our method outperforms the state-of-the-art depth image super-resolution methods with a margin. ABSTRACT In the last two decades, two related categories of problems have been studied independently in the image restora-tion literature: super-resolution and demosaicing. A closer look at these problems reveals the relation betweenthem, and as conventional color digital cameras suer from both low-spatial resolution and color ltering, it isreasonable to address them in a unied context. In this paper, we propose a fast and robust hybrid method ofsuper-resolution and demosaicing, based on a maximum a posteriori (MAP) estimation technique by minimizinga multi-term cost function. The L 1 norm is used for measuring the dierence between the projected estimate ofthe high-resolution image and each low-resolution image, removing outliers in the data and errors due to possiblyinaccurate motion estimation. Bilateral regularization is used for regularizing the luminance component, result-ing in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonovregularization is used to smooth the chrominance component. Finally, an additional regularization term is usedto force similar edge orientation in dierent color channels. We show that the minimization of the total costfunction is relatively easy and fast. Experimental results on synthetic and real data sets conrm the eectivenessof our method.Keywords: Super-Resolution, Demosaicing, Robust Estimation, Robust Regularization, Color Enhancement