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. © 2008 IEEE.

A. N. Rajagopalan

Department of Electrical Engineering

Rangarajan Aravind

Agricultural products

Canning

Control theory

Kalman filters

Military data processing

Nonlinear filtering

PHOTOGRAPHIC FILMS

Wave filters

Edge Information

FILM-GRAIN NOISE

IMPORTANCE SAMPLING (IS)

NON GAUSSIAN NOISES

Unscented Kalman Filter (UKF)

Gaussian noise (electronic)

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Importance Sampling-Based Unscented Kalman Filter for Film-Grain Noise Removal

IEEE Multimedia

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. Different filter formulations with a discontinuity-adaptive Markov random field prior are proposed for handling additive white Gaussian noise and film-grain noise. © 2006 IEEE.

IEEE Transactions on Image Processing

Natural matting for degraded pictures

In this paper, we propose a video matting method with simultaneous noise reduction based on the Unscented Kalman filter (UKF). This recursive approach extracts the alpha mattes and denoised foregrounds from noisy videos, in a unified framework. No assumptions are made about the type of motion of the camera or of the foreground object in the video. Moreover, user-specified trimaps are required only once every ten frames. In order to accurately extract information at the borders between the foreground and the background, we include a discontinuity-adaptive Markov random field (MRF) prior. It incorporates spatio-temporal information from the current and previous frame during estimation of the alpha matte as well as the foreground. Results are given on videos with real film-grain noise. © 2010 IEEE.

Proceedings - International Conference on Pattern Recognition

Recursive video matting and denoising

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. © 2010 Optical Society of America.

Journal of the Optical Society of America A: Optics and Image Science, and Vision

Recursive framework for joint inpainting and de-noising of photographic films

A new two-dimensional recursive filter for recovering degraded images is proposed that is based on particle-filter theory. The main contribution of this work lies in evolving a framework that has the potential to recover images suffering from a general class of degradations such as system nonlinearity and non-Gaussian observation noise. Samples of the prior probability distribution of the original image are obtained by propagating the samples through an appropriate state model. Given the measurement model and the degraded image, the weights of the samples are computed. The samples and their corresponding weights are used to calculate the conditional mean that yields an estimate of the original image. The proposed method is validated by demonstrating its effectiveness in recovering images degraded by film-grain noise. Synthetic as well as real examples are considered for this purpose. Performance is also compared with that of an existing scheme. © 2005 Optical Society of America.

Fulltext

Image recovery under nonlinear and non-Gaussian degradations

ljournal

МАТЕРИАЛЫ

2006 International Conference on Image Processing

Denoising Archival Films using a Learned Bayesian Model

Signal Processing

MMSE filtering of generalised signal-dependent noise in spatial and shift-invariant wavelet domains

Digital Watermarking Lecture Notes in Computer Science

Data Hiding in Film Grain

Importance sampling-based unscented Kalman filter for film-grain noise removal

Journal	IEEE Multimedia
ISSN	1070986X
Open Access	No