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X-ray computed tomography (XCT) is the only non-destructive technique able to perform a complete quality control of additive manufactured products in a single inspection. Yet, high-quality scans are associated with large acquisition times and limited to high-end AM parts. In this paper we investigate a deep learning U-net segmentation algorithm to improve the segmentation of low-quality XCT scans. A high-quality XCT scan is acquired and aligned with low-quality XCT scans to create the ground truth data. The accuracy of the segmentation is quantified with the Jaccard index and physical properties of the parts.

Recently, several works have proposed fusing radar data as an additional perceptual signal into monocular depth estimation models because radar data is robust against varying light and weather conditions. Although improved performances were reported in prior works, it is still hard to tell how much depth information radar can contribute to a depth estimation model. In this paper, we propose radar inference and supervision experiments to investigate the intrinsic depth potential of radar data using state-of-the-art depth estimation models on the nuScenes dataset. In the inference experiment, the model predicts depth by taking only radar as input to demonstrate the inference capability using radar data. In the supervision experiment, a monocular depth estimation model is trained under radar supervision to show the intrinsic depth information that radar can contribute. Our experiments demonstrate that the model using only sparse radar as input can detect the shape of surroundings to a certain extent in the predicted depth. Furthermore, the monocular depth estimation model supervised by preprocessed radar achieves a good performance compared to the baseline model trained with sparse lidar supervision., published on EI2023, 7 pages, 4 figures, 2 tables

Image Super-Resolution: Historical Overview and Future Challenges, J. Yang and T. Huang Introduction to Super-Resolution Notations Techniques for Super-Resolution Challenge issues for Super-Resolution Super-Resolution Using Adaptive Wiener Filters, R.C. Hardie Introduction Observation Model AWF SR Algorithms Experimental Results Conclusions Acknowledgments Locally Adaptive Kernel Regression for Space-Time Super-Resolution, H. Takeda and P. Milanfar Introduction Adaptive Kernel Regression Examples Conclusion AppendiX Super-Resolution With Probabilistic Motion Estimation, M. Protter and M. Elad Introduction Classic Super-Resolution: Background The Proposed Algorithm Experimental Validation Summary Spatially Adaptive Filtering as Regularization in Inverse Imaging, A. Danielyan, A. Foi, V. Katkovnik, and K. Egiazarian Introduction Iterative filtering as regularization Compressed sensing Super-resolution Conclusions Registration for Super-Resolution, P. Vandewalle, L. Sbaiz, and M. Vetterli Camera Model What Is Resolution? Super-Resolution as a Multichannel Sampling Problem Registration of Totally Aliased Signals Registration of Partially Aliased Signals Conclusions Towards Super-Resolution in the Presence of Spatially Varying Blur, M. Sorel, F. Sroubek and J. Flusser Introduction Defocus and Optical Aberrations Camera Motion Blur Scene Motion Algorithms Conclusion Acknowledgments Toward Robust Reconstruction-Based Super-Resolution, M. Tanaka and M. Okutomi Introduction Overviews Robust SR Reconstruction with Pixel Selection Robust Super-Resolution Using MPEG Motion Vectors Robust Registration for Super-Resolution Conclusions Multi-Frame Super-Resolution from a Bayesian Perspective, L. Pickup, S. Roberts, A. Zisserman and D. Capel The Generative Model Where Super-Resolution Algorithms Go Wrong Simultaneous Super-Resolution Bayesian Marginalization Concluding Remarks Variational Bayesian Super Resolution Reconstruction, S. Derin Babacan, R. Molina, and A.K. Katsaggelos Introduction Problem Formulation Bayesian Framework for Super Resolution Bayesian Inference Variational Bayesian Inference Using TV Image Priors Experiments Estimation of Motion and Blur Conclusions Acknowledgements Pattern Recognition Techniques for Image Super-Resolution, K. Ni and T.Q. Nguyen Introduction Nearest Neighbor Super-Resolution Markov Random Fields and Approximations Kernel Machines for Image Super-Resolution Multiple Learners and Multiple Regressions Design Considerations and Examples Remarks Glossary Super-Resolution Reconstruction of Multi-Channel Images, O.G. Sezer and Y. Altunbasak Introduction Notation Image Acquisition Model Subspace Representation Reconstruction Algorithm Experiments & Discussions Conclusion New Applications of Super-Resolution in Medical Imaging, M.D.Robinson, S.J. Chiu, C.A. Toth, J.A. Izatt, J.Y. Lo, and S. Farsiu Introduction The Super-Resolution Framework New Medical Imaging Applications Conclusion Acknowledgment Practicing Super-Resolution: What Have We Learned? N. Bozinovic Abstract Introduction MotionDSP: History and Concepts Markets and Applications Technology Results Lessons Learned Conclusions

ispartof: Electronic Imaging 2020 ispartof: 3D Measurement and Data Processing 2020 location:Burlingame, California USA status: Published online

The relation between sensor resolution and the optics of a digital camera is determined by the Nyquist sampling theorem: the sampling frequency should be larger than twice the maximum frequency of the image content coming out of the optical system. If a lower resolution is used, the output is aliased. Aliasing in digital images is often considered as a nuisance and (both optical and digital) filters are designed to avoid aliasing in digital cameras. However, aliasing also contains extra high-frequency information with additional details about the scene. Super-resolution algorithms extract the information present in the aliasing to reconstruct a higher resolution image. Super-resolution algorithms typically combine multiple aliased images with small relative motion, and create a single high resolution image. The input can be a set of pictures taken with a digital camera from approximately the same point of view. An application could be to use a low resolution camera (with a good optical system), capture a set of images while holding the camera manually in approximately the same position, and use the small camera shake to reconstruct a high resolution image. This would allow to take multiple images with a cheap camera, and combine them to a higher resolution image as if it had been taken with a more expensive camera. Other applications can be found for example in situations where a camera sensor can not be easily replaced, such as in satellites. It is (almost) impossible to install a new camera sensor, while a modification of the software allows to take a series of images of approximately the same subject.

What should we do to raise the quality of signal processing publications to an even higher level? We believe it to be crucial to maintain the precision in describing our work in publications, ensured through a high-quality reviewing process. We also believe that if the experiments are performed on a large data set, the algorithm is compared to the state-of-the-art methods, the code and/or data are well documented and available online, we will all benefit and make it easier to build upon each other's work. It is a clear win-win situation for our community: we will have access to more and more algorithms and can spend time inventing new things rather than recreating existing ones.

© 2014 IEEE. While a wide range of disparity estimation algorithms exists, only few methods are available to analyze the quality of a disparity map. We present a new two-dimensional approach to disparity map evaluation using the stereo matching error and the temporal error. These measures give a good indication of stereo or multi-view performance on a 3D display. Results are illustrated on a ground truth disparity map with multiple distortions as well as on a commercially available disparity estimator using varying post-processing settings on a real stereoscopic video sequence. ispartof: 2014 International Conference on 3D Imaging, IC3D 2014 - Proceedings ispartof: International Conference on 3D Imaging (IC3D) location:BELGIUM, Liege date:9 Dec - 10 Dec 2014 status: published

We introduce supervised disparity estimation in which an operator can steer the disparity estimation process. Instead of correcting errors, we view the estimation process as a constrained process where the constraints are indicated by the user in the form of control points, scribbles and contours. Control points are used to obtain accurate disparity estimates that can be fully controlled by the operator. Scribbles are used to force regions to have a smooth disparity, while contours create a disparity discontinuity in places where diffusion or energy minimization fail. Control points, scribbles and contours are propagated through the video sequence to create temporally stable results. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE). ispartof: Proceedings of SPIE - The International Society for Optical Engineering vol:8288 ispartof: Conference on Stereoscopic Displays and Applications XXIII (SD and A)/IS and T/SPIE Electronic Imaging - Science and Technology Symposium location:CA, Burlingame date:23 Jan - 25 Jan 2012 status: published

We introduce a setup for detailedand unobtrusive feet mapping in a room. Two cameras arranged in a stereo pair detect when an object touches ground by analyzing the occlusions on a fluorescent tape that is attached to the baseboard of a room. The disparity between both cameras allows localization of a person's feet and the calculation of step size and walking speed. People areseparated from furniture such as chairs and tables by studying the occlusion duration. We present and discuss data-association and filtering algorithms and the algorithms that are needed for presence detection, step characterization and gaming. © ACM 2010. ispartof: ACM International Conference Proceeding Series status: published

This paper discusses an approach to 3D-Television that is based on the Layered Depth Video (LDV) format. The LDV format contains explicit depth and occlusion information, allowing for the generation of novel viewpoints for stereoscopic and auto-stereoscopic multi-view displays. Thus, the format is effectively invariant to the display type and also allows the depth impression to be easily changed to best meet viewers' preferences for visual comfort. The major aspects of a content delivery chain based on the LDV format are discussed in this paper. The requirements placed on data acquisition are introduced, and a multi-camera system, which is well suited for LDV compliant data capture, is presented. Also discussed is the conversion of different input data streams, like standard stereo videos, multi-view data supplemented by depth data, and videos from wide baseline setups, to the LDV format. Moreover, the advantages of the LDV format in editing and mixing are examined. The paper also presents a transmission system based on currently available coding and transmission standards. Optimization of the bandwidth via different approaches to the compression of the LDV signal is analyzed, and the results of conducted experiments in this field are discussed. Finally, the aspects of perceptual human factors for the proper evaluation of 3D-TV services and the implemented LDV system are examined. This contribution reflects the efforts of the EU-funded project 3D4YOU to unify all aspects of 3D-TV production.

A vastly growing number of productions from the entertainment industry are aiming at 3D movie theaters. These productions use a two-view format, primarily intended for eye-wear assisted viewing in a well defined environment. To get this 3D content into the home environment, where a large variety of 3D viewing conditions exists (e.g. different display sizes, display types, viewing distances), we need a flexible 3D format that can adjust the depth effect. This can be provided by the image plus depth format, in which a video frame is enriched with depth information for all pixels in the video frame. This format can be extended with additional layers, such as an occlusion layer or a transparency layer. The occlusion layer contains information on the data that is behind objects, and is also referred to as occluded video. The transparency layer, on the other hand, contains information on the opacity of the foreground layer. This allows rendering of semi-transparencies such as haze, smoke, windows, etc., as well as transitions from foreground to background. These additional layers are only beneficial if the quality of the depth information is high. High quality depth information can currently only be achieved with user assistance. In this paper, we discuss an interactive method for depth map enhancement that allows adjustments during the propagation over time. Furthermore, we will elaborate on the automatic generation of the transparency layer, using the depth maps generated with an interactive depth map generation tool. © 2010 Copyright SPIE - The International Society for Optical Engineering. ispartof: Proceedings of SPIE - The International Society for Optical Engineering vol:7524 ispartof: Conference on Stereoscopic Displays and Applications XXI location:CA, San Jose date:18 Jan - 20 Jan 2010 status: published

It is generally understood that interpreting depth from monoscopic images inherently induces the problem of object segmentation and context recognition. A common way to deal with this complexity is to focus on one aspect (e.g. segmentation) and largely constrain or skip the other (e.g. scene classification). In our approach, we practically reconcile the two paths. We start by defining several depth profiles, providing rough estimates for both background and foreground, while reflecting common photographic and cinematic practices and rules. The idea is to assign one or more of these profiles to an image - based on the output of multiple classifiers, and apply an image-adaptive bilateral filter to align the depth with object edges. From extensive trials, we conclude that robust classification and visual performance can be achieved with this approach on a variety of content. ©2009 IEEE. ispartof: pages:2365-2368 ispartof: Proceedings - International Conference on Image Processing, ICIP pages:2365-2368 ispartof: 16th IEEE International Conference on Image Processing location:EGYPT, Cairo date:7 Nov - 10 Nov 2009 status: published

We propose an interface for creating a depth map for a 2D picture. The image and depth map can be used for 3D display on an auto-stereoscopic photo frame. Our new interface does not require the user to draw on the picture or point at an object in the picture. Instead, semantic questions are asked about a given indicated position in the picture. This semantic information is then translated automatically into a depth map. ©2009 IEEE. ispartof: pages:13-+ ispartof: 3DTV-CON 2009 - 3rd 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, Proceedings pages:13-+ ispartof: 3DTV Conference - True Vision - Capture, Transmission and Display of 3D Video location:GERMANY, Potsdam date:4 May - 6 May 2009 status: published