369 research outputs found
Depth Superresolution using Motion Adaptive Regularization
Spatial resolution of depth sensors is often significantly lower compared to
that of conventional optical cameras. Recent work has explored the idea of
improving the resolution of depth using higher resolution intensity as a side
information. In this paper, we demonstrate that further incorporating temporal
information in videos can significantly improve the results. In particular, we
propose a novel approach that improves depth resolution, exploiting the
space-time redundancy in the depth and intensity using motion-adaptive low-rank
regularization. Experiments confirm that the proposed approach substantially
improves the quality of the estimated high-resolution depth. Our approach can
be a first component in systems using vision techniques that rely on high
resolution depth information
Signal reconstruction via operator guiding
Signal reconstruction from a sample using an orthogonal projector onto a
guiding subspace is theoretically well justified, but may be difficult to
practically implement. We propose more general guiding operators, which
increase signal components in the guiding subspace relative to those in a
complementary subspace, e.g., iterative low-pass edge-preserving filters for
super-resolution of images. Two examples of super-resolution illustrate our
technology: a no-flash RGB photo guided using a high resolution flash RGB
photo, and a depth image guided using a high resolution RGB photo.Comment: 5 pages, 8 figures. To appear in Proceedings of SampTA 2017: Sampling
Theory and Applications, 12th International Conference, July 3-7, 2017,
Tallinn, Estoni
A multi-frame super-resolution method based on the variable-exponent nonlinear diffusion regularizer
Super-Resolution Textured Digital Surface Map (DSM) Formation by Selecting the Texture From Multiple Perspective Texel Images Taken by a Low-Cost Small Unmanned Aerial Vehicle (UAV)
Textured Digital Surface Model (TDSM) is a three-dimensional terrain map with texture overlaid on it. Utah State University has developed a texel camera which can capture a 3D image called a texel image. A TDSM can be constructed by combining these multiple texel images, which is much cheaper than the traditional method. The overall goal is to create a TDSM for a larger area that is cheaper and equally accurate as the TDSM created using a high-cost system.
The images obtained from such an inexpensive camera have a lot of errors. To create scientifically accurate TDSM, the error presented in the image must be corrected. An automatic process to create TDSM is presented that can handle a large number of input texel images. The advantage of using such a large set of input images is that they can cover a large area on the ground, making the algorithm suitable for large-scale applications. This is done by processing images and correcting them in a windowing manner. Furthermore, the appearance of the final 3D terrain map is improved by selecting the texture from many candidate images. This ensures that the best texture is selected. The selection criteria are discussed. Lastly, a method to increase the resolution of the final image is discussed. The methods described in this dissertation improve the current technique of creating TDSM, and the results are shown and analyzed
Image Restoration
This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with
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