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.

A. N. Rajagopalan

Department of Electrical Engineering

Rangarajan Aravind

Photography

De-noising

FILM-GRAIN NOISE

IMAGE EDGE

IMAGE RECOVERY

Inpainting

Markov Random Fields

MISSING OBSERVATIONS

Noise reductions

Unscented Kalman Filter

PHOTOGRAPHIC FILMS

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

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

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

Reconstruction of a super-resolved image from multiple frames and extraction of matte are two popular topics that have been solved independently. In this paper, we advocate a unified framework that assimilates matting within the super-resolution model. We show 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. We propose a multiframe approach to increase the spatial resolution of the matte, foreground, and background. This is validated extensively on examples from standard matting datasets. © 2013 Optical Society of America.

Unified multiframe super-resolution of matte, foreground, and background

Range images often suffer from issues such as low resolution (LR) (for low-cost scanners) and presence of missing regions due to poor reflectivity, and occlusions. Another common problem (with high quality scanners) is that of long acquisition times. In this work, we propose two approaches to counter these shortcomings. Our first proposal which addresses the issues of low resolution as well as missing regions, is an integrated super-resolution (SR) and inpainting approach. We use multiple relatively-shifted LR range images, where the motion between the LR images serves as a cue for super-resolution. Our imaging model also accounts for missing regions to enable inpainting. Our framework models the high resolution (HR) range as a Markov random field (MRF), and uses inhomogeneous MRF priors to constrain the solution differently for inpainting and super-resolution. Our super-resolved and inpainted outputs show significant improvements over their LR/interpolated counterparts. Our second proposal addresses the issue of long acquisition times by facilitating reconstruction of range data from very sparse measurements. Our technique 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. Our approach is able to reconstruct range images with as little as 10% data. We also study the performance of both the proposed approaches in a noisy scenario as well as in the presence of alignment errors. © 2011 Elsevier Inc. All rights reserved.

Computer Vision and Image Understanding

Range map superresolution-inpainting, and reconstruction from sparse data

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.

IEEE Multimedia

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

A method based on the particle filter for recovering images degraded by film- grain noise is proposed. Due to the non-linear relationship between the silver density and exposure, film-grain noise manifests itself as multiplicative non- Gaussian noise in the exposure domain. Since the posterior density is non- Gaussian, the proposed method works by representing it by a set of samples with associated weights. These samples are propagated in a recursive framework to obtain an optimal estimate of the original image. The effectiveness of the method is demonstrated with examples. © 2005 IEEE.

Journal	Data powered by TypesetJournal of the Optical Society of America A: Optics and Image Science, and Vision
Publisher	Data powered by TypesetOSA - The Optical Society
ISSN	10847529
Impact Factor	1.230
Open Access	No
Citation Style	unsrt
Sherpa RoMEO Archiving Policy	Green