One popular method for the recovery of an ideal intensity image from corrupted or indirect measurements is regularization: minimize an objective function which enforces a roughness penalty in addition to coherence with the data. Linear estimates are relatively easy to compute but generally introduce systematic errors; for example, they are incapable of recovering discontinuities and other important image attributes. In contrast, nonlinear estimates are more accurate, but often far less accessible. This is particularly true when the objective function is non-convex and the distribution of each data component depends on many image components through a linear operator with broad support. Our approach is based on an auxiliary array and an extended objective function in which the original variables appear quadratically and the auxiliary variables are decoupled. Minimizing over the auxiliary array alone yields the original function, so the original image estimate can be obtained by joint min...

This dissertation presents algorithms for restoring some of the major corruptions observed in archived film or video material. The two principal problems of impulsive distortion (Dirt and Sparkle or Blotches) and noise degradation are considered. There is also an algorithm for suppressing the inter--line jitter common in images decoded from noisy video signals. In the case of noise reduction and Blotch removal the thesis considers image sequences to be three dimensional signals involving evolution of features in time and space. This is necessary if any process presented is to show an improvement over standard two--dimensional techniques. It is important to recognize that consideration of image sequences must involve an appreciation of the problems incurred by the motion of objects in the scene. The most obvious implication is that due to motion, useful three dimensional processing does not necessarily proceed in a direction `orthogonal' to the image frames. Therefore, attention is giv...

This thesis addresses a number of issues concerned with the restoration of one type of image sequence, namely archived black and white motion pictures. These are often a valuable historical record, but because of the physical nature of the film they can suffer from a variety of degradations which reduce their usefulness. The main visual defects are `dirt and sparkle' due to dust and dirt becoming attached to the film, or abrasion removing the emulsion, and `line scratches' due to the film running against foreign bodies in the camera or projector. For an image

Line scratches are common degradations in motion picture films. This paper presents an efficient method for line scratches detection strengthened by a Kalman filter. A new interpolation technique, dealing with both low and high frequencies (i.e. film grain) around the line artifacts, is investigated to achieve a nearby invisible reconstruction of damaged areas. Our line scratches detection and removal techniques have been validated on several film sequences. 1 Introduction Motion picture industry is 100 years old and chemical film (nowadays polyester, former triacetate or nitrate) is still the main support for motion picture film, despite of fast-growing digital media. It is a fact the film and dye coating for color are not stable over decades. In addition, some old films are reassembled from fragments scattered over various libraries and archives. Only digital processing will ensure the removal of the various artifacts due to age and a well balanced output to a film recorder. The ma...

Multispectral images consist of multiple channels, each containing data acquired from a different band within the frequency spectrum. Since most objects emit or reflect energy over a large spectral bandwidth, there usually exists a significant correlation between channels. Due to often harsh imaging environments, the acquired data may be degraded by both blur and noise. Simply applying a monochromatic restoration algorithm to each frequency band ignores the cross-channel correlation present within a multispectral image. A Gibbs prior is proposed for multispectral data modeled as a Markov random field, containing both spatial and spectral cliques. Spatial components of the model use a nonlinear operator to preserve discontinuities within each frequency band, while spectral components incorporate nonstationary cross-channel correlations. The multispectral model is used in a Bayesian algorithm for the restoration of color images, in which the resulting nonlinear estimates are shown to be ...

this article is to discuss general reasons for this prominence of MCMC, to give an overview of a variety of image models and the use made of MCMC methods in dealing with them, to describe two applications in more detail, To appear as a chapter in the book Practical Markov chain Monte Carlo, edited by W. Gilks, S. Richardson and D. Spiegelhalter, published by Chapman and Hall.

This paper presents 3-D (spatiotemporal) Kalman filters for video as the extension of the 2-D reduced update Kalman filter (RUKF) approach for images. We start out with 3-D RUKF, a shift-invariant recursive estimator with efficiency advantages over the 3-D Wiener filter. Then, we turn to the motion compensated extension MC-RUKF, which gives improved performance when coupled with a motion estimator. Since motion compensation sometimes fails, causing severe fluctuations in temporal correlation, we then present multi-model MC-RUKF, to adapt to variation in temporal and spatial correlation, by detecting the local image model out of a class, and using it in MC-RUKF. Finally, we introduce a novel multiscale model detection algorithm, for use in high noise environments. Keywords--- spatiotemporal adaptive Kalman filter, motion compensation, reduced update Kalman filter, multi-model, multiscale model detection. I. INTRODUCTION In a noise-free video with little motion, each pair of consecuti...

Many archived motion pictures suffer from what we term replacement noise, that is various degradations such as dirt, scratches, fingerprints etc, where the original picture is replaced by some unrelated information. In this paper we use markov random field based motion field segmentation to detect these areas, and then interpolate into the gaps using a motion-compensated interpolation scheme to restore the frame. INTRODUCTION This paper is concerned with replacement noise, that is, dirt, scratches and other extended regions of the frame where the original grey level information is lost. This form of degradation is often visually more annoying than global white noise and is very common in archived motion pictures. From a single frame a human observer can detect replacement noise using contextual information; our signal processing solution relies on the redundancy inherent in the image sequence -- the grey level observed in an area affected by replacement noise `does not fit' with the r...

Nonlinear regression and nonlinear regularization are two powerful approaches to segmentation and nonlinear filtering. In this correspondence, we propose a hybrid approach that effectively combines the best of both worlds and can be efficiently implemented via the Viterbi algorithm. I.

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