Journal ArticleDOI
Importance Sampling-Based Unscented Kalman Filter for Film-Grain Noise Removal
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TLDR
A method based on the unscented Kalman filter can suppress this noise while simultaneously preserving edge information in the exposure domain.Abstract:
Photographic film contains film-grain noise that translates to multiplicative, non-Gaussian noise in the exposure domain. A method based on the unscented Kalman filter can suppress this noise while simultaneously preserving edge information.read more
Citations
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Journal ArticleDOI
Recursive framework for joint inpainting and de-noising of photographic films
TL;DR: 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.
Journal ArticleDOI
Natural Matting for Degraded Pictures
TL;DR: A new approach for image matting is proposed based on the Kalman filter, to extract the matte and original foreground, despite the presence of noise in the observed image.
Journal ArticleDOI
A model for signal processing and predictive control of semi-active structural control system
TL;DR: In this paper, a predictive control technique that integrates an improved method of detecting the control signal based on the direction of the structural motion, and a calculator for detecting the velocity using the least-square polynomial regression is proposed.
Journal ArticleDOI
Image denoising using DLNN to recognize the direction of pixel variation
TL;DR: A deep-learning neural network is applied to determine the pixel-variation direction for noisy image denoising to effectively remove interference noise in a noisy image with noise density ranging from 10 to 90%.
Proceedings ArticleDOI
Recursive Video Matting and Denoising
TL;DR: A video matting method with simultaneous noise reduction based on the Unscented Kalman filter (UKF) that incorporates spatio-temporal information from the current and previous frame during estimation of the alpha matte as well as the foreground.
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.
Book
Digital Image Restoration
TL;DR: The article introduces digital image restoration to the reader who is just beginning in this field, and provides a review and analysis for the readers who may already be well-versed in image restoration.
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.
Book ChapterDOI
Introduction to Monte Carlo methods
TL;DR: In this paper, a sequence of Monte Carlo methods, namely importance sampling, rejection sampling, the Metropolis method, and Gibbs sampling, are described and a discussion of advanced methods, including methods for reducing random walk behaviour is presented.
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