653 research outputs found
Wavelet Constrained Regularization For Disparity Map Estimation
This paper describes a novel method for estimating dense disparity maps, based on wavelet representations
Disparity and Optical Flow Partitioning Using Extended Potts Priors
This paper addresses the problems of disparity and optical flow partitioning
based on the brightness invariance assumption. We investigate new variational
approaches to these problems with Potts priors and possibly box constraints.
For the optical flow partitioning, our model includes vector-valued data and an
adapted Potts regularizer. Using the notation of asymptotically level stable
functions we prove the existence of global minimizers of our functionals. We
propose a modified alternating direction method of minimizers. This iterative
algorithm requires the computation of global minimizers of classical univariate
Potts problems which can be done efficiently by dynamic programming. We prove
that the algorithm converges both for the constrained and unconstrained
problems. Numerical examples demonstrate the very good performance of our
partitioning method
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
We consider the problem of dense depth prediction from a sparse set of depth
measurements and a single RGB image. Since depth estimation from monocular
images alone is inherently ambiguous and unreliable, to attain a higher level
of robustness and accuracy, we introduce additional sparse depth samples, which
are either acquired with a low-resolution depth sensor or computed via visual
Simultaneous Localization and Mapping (SLAM) algorithms. We propose the use of
a single deep regression network to learn directly from the RGB-D raw data, and
explore the impact of number of depth samples on prediction accuracy. Our
experiments show that, compared to using only RGB images, the addition of 100
spatially random depth samples reduces the prediction root-mean-square error by
50% on the NYU-Depth-v2 indoor dataset. It also boosts the percentage of
reliable prediction from 59% to 92% on the KITTI dataset. We demonstrate two
applications of the proposed algorithm: a plug-in module in SLAM to convert
sparse maps to dense maps, and super-resolution for LiDARs. Software and video
demonstration are publicly available.Comment: accepted to ICRA 2018. 8 pages, 8 figures, 3 tables. Video at
https://www.youtube.com/watch?v=vNIIT_M7x7Y. Code at
https://github.com/fangchangma/sparse-to-dens
Assessment of a Neural Network-Based Subspace MRI Reconstruction Method for Myocardial T1 Mapping Using Inversion-Recovery Radial FLASH
openLa mappatura T1 del miocardio si è affermata come un promettente biomarker per la caratterizzazione non invasiva del muscolo cardiaco nell'ambito della risonanza magnetica cardiovascolare. Questo approccio ha il potenziale di sostituire la biopsia nella diagnosi di diverse condizioni patologiche del miocardio, come la fibrosi, l'accumulo di ferro o amiloidosi. Negli ultimi anni, il deep learning ha suscitato un crescente interesse per la ricostruzione delle immagini, portando a notevoli miglioramenti rispetto alle tecniche che richiedono la predefinizione dei parametri di regolarizzazione da parte dell'operatore, rendendo così il processo parzialmente soggettivo. Il miglioramento è reso possibile grazie alla capacità delle reti neurali di apprendere automaticamente le proprietà presenti nelle immagini del dataset utilizzato per il training. La presente tesi si focalizza sull'analisi di un nuovo metodo di ricostruzione subspaziale delle immagini di risonanza magnetica basato su reti neurali per la mappatura T1 del miocardio, che utilizza una sequenza chiamata single-shot inversion-recovery radial FLASH. È stata impiegata una rete neurale nota come NLINV-Net, la quale trae ispirazione dalla tecnica di ricostruzione delle immagini NLINV. NLINV-Net risolve il problema inverso non lineare per il parallel imaging srotolando l'iteratively regularized Gauss-Newton method e incorporando nel processo termini di regolarizzazione basati su reti neurali. La rete neurale ha appreso le correlazioni esistenti tra i singoli parametri codificati dalla sequenza FLASH in modo auto-supervisionato, ovvero senza richiedere un riferimento esterno. NLINV-Net ha dimostrato di superare NLINV per la precisione dei valori T1, producendo mappe T1 di alta qualità. Le mappe ottenute con NLINV-Net sono paragonabili a quelle ottenute con un altro metodo di riferimento, che combina parallel imaging e compressed sensing utilizzando la regolarizzazione l1-Wavelet nella risoluzione del problema lineare inverso per il parallel imaging. Il vantaggio di NLINV-Net rispetto al suddetto metodo di appoggio è quello di sbarazzarsi della predefinizione dei parametri di regolarizzazione da parte dell'operatore. In questo modo, NLINV-Net fornisce una solida base per la mappatura T1 del miocardio utilizzando la sequenza single-shot inversion-recovery radial FLASH.In cardiovascular MRI, myocardial T1 mapping provides an imaging biomarker for the non-invasive characterization of the myocardial tissue, with the potential to replace invasive biopsy for the diagnosis of several pathological heart muscle conditions such as fibrosis, iron overload, or amyloid infiltration. Over the last few years, deep learning has become increasingly appealing for image reconstruction to improve upon the commonly employed user-dependent regularization terms by automatically learning image properties from the training dataset. This thesis investigates a novel neural network-based subspace MRI reconstruction method for myocardial T1 mapping, which uses a single-shot inversion-recovery radial FLASH sequence. The neural network utilized in this study is NLINV-Net, which draws inspiration from the NLINV image reconstruction technique. NLINV-Net addresses the nonlinear inverse problem for parallel imaging by unrolling the iteratively regularized Gauss-Newton method and incorporating neural network-based regularization terms into the process. It learned in a self-supervised fashion, i.e., without a reference, correlations between the individual parameters encoded with the FLASH sequence, and, consequently, a well-tuned regularization. NLINV-Net outperformed NLINV in terms of T1 precision and generated high-quality T1 maps. The T1 maps computed using NLINV-Net were comparable to the ones obtained using another baseline method, which combines parallel imaging and compressed sensing using the l1-Wavelet regularization when solving the linear inverse problem for parallel imaging. In this case, the advantage of NLINV-Net is that it removes the subjective regularization parameter tuning that comes with the forenamed benchmark method. Thus, it provides an excellent basis for myocardial T1 mapping using a single-shot inversion-recovery radial FLASH sequence
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Robust Obstacle Detection based on Dense Disparity Maps
Obstacle detection is an important component for many autonomous vehicle navigation systems. Several methods for obstacle detection have been proposed using various active sensors such as radar, sonar and laser range finders. Vision based techniques have the advantage of low cost and provide a large amount of information about the environment around an intelligent vehicle. This paper deals with the development of an accurate and efficient vision based obstacle detection method which relies on a wavelet analysis. The development system will be integrated on the Cybercar platform which is a road vehicle with fully automated driving capabilities
Disparity and optical flow partitioning using extended Potts priors
This paper addresses the problems of disparity and optical flow partitioning based on the brightness invariance assumption. We investigate new variational approaches to these problems with Potts priors and possibly box constraints. For the optical flow partitioning, our model includes vector-valued data and an adapted Potts regularizer. Using the notion of asymptotically level stable (als) functions, we prove the existence of global minimizers of our functionals. We propose a modified alternating direction method of multipliers. This iterative algorithm requires the computation of global minimizers of classical univariate Potts problems which can be done efficiently by dynamic programming. We prove that the algorithm converges both for the constrained and unconstrained problems. Numerical examples demonstrate the very good performance of our partitioning method
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