Journal ArticleDOI
Recursive framework for joint inpainting and de-noising of photographic films
TLDR
A recursive image recovery scheme based on the unscented Kalman filter (UKF) to simultaneously inpaint identified damaged portions in an image and suppress film-grain noise is presented.Abstract:
We address the problem of inpainting noisy photographs. We present a recursive image recovery scheme based on the unscented Kalman filter (UKF) to simultaneously inpaint identified damaged portions in an image and suppress film-grain noise. Inpainting of the missing observations is guided by a mask-dependent reconstruction of the image edges. Prediction within the UKF is based on a discontinuity-adaptive Markov random field prior that attempts to preserve edges while achieving noise reduction in uniform regions. We demonstrate the capability of the proposed method with many examples.read more
Citations
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Journal ArticleDOI
Range map superresolution-inpainting, and reconstruction from sparse data
Arnav Bhavsar,A. N. Rajagopalan +1 more
TL;DR: This work uses multiple relatively-shifted LR range images, where the motion between the LR images serves as a cue for super-resolution, and exploits a cue from segmentation of an optical image of the same scene, which constrains pixels in the same color segment to have similar range values.
Journal ArticleDOI
Restoration of digital off-axis Fresnel hologram by exemplar and search based image inpainting with enhanced computing speed
TL;DR: Experimental results reveal that with the proposed method, the pictorial information in heavily damaged holograms can be recovered with good fidelity, as compared with those obtained with the original intact holograms.
Book ChapterDOI
A Non-local Method for Robust Noisy Image Completion
TL;DR: This paper proposes a non-local patch-based algorithm to settle the noisy image completion problem following the methodology “grouping and collaboratively filtering”, which achieves state-of-the-art performance in terms of both PSNR and subjective visual quality.
Proceedings ArticleDOI
Processing of digital holograms: segmentation and inpainting
Shuming Jiao,Wenbin Zou +1 more
TL;DR: A Virtual Diffraction Plane based hologram decomposition scheme is proposed based on Otsu thresholding segmentation, morphological dilation and sequential scan labelling and potential applications of hologram inpainting are discussed.
Journal ArticleDOI
Unified multiframe super-resolution of matte, foreground, and background
TL;DR: This paper proposes a multiframe approach to increase the spatial resolution of the matte, foreground, and background and shows that joint estimation is advantageous, as super-resolved edge information helps in obtaining a sharp matte, while the matte in turn aids in resolving fine details.
References
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Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Proceedings ArticleDOI
New extension of the Kalman filter to nonlinear systems
Simon Julier,Jeffrey Uhlmann +1 more
TL;DR: It is argued that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.
Proceedings ArticleDOI
Image inpainting
TL;DR: A novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators, and does not require the user to specify where the novel information comes from.
Proceedings Article
The Unscented Particle Filter
TL;DR: This paper proposes a new particle filter based on sequential importance sampling that outperforms standard particle filtering and other nonlinear filtering methods very substantially and is in agreement with the theoretical convergence proof for the algorithm.
Book
Markov Random Field Modeling in Computer Vision
TL;DR: This book presents a comprehensive study on the use of MRFs for solving computer vision problems, and covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms.