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Abstract

Bright and dark flashes are typical artifacts in degraded motion picture material. The distortion is referred to as "dirt and sparkle" in the motion picture industry. This is caused either by dirt becoming attached to the frames of the film, or by the film material being abraded. The visual result is random patches of the frames having grey level values totally unrelated to the initial information at those sites. To restore the film without causing distortion to areas of the frames that are not affected, the locations of the blotches must be identified. Heuristic and model-based methods for the detection of these missing data regions are presented in this paper, and their action on simulated and real sequences is compared.

A common task in image editing is to change the colours of a picture to match the desired colour grade of another picture. Finding the correct colour mapping is tricky because it involves numerous interrelated operations, like balancing the colours, mixing the colour channels or adjusting the contrast. Recently, a number of automated tools have been proposed to find an adequate one-to-one colour mapping. The focus in this paper is on finding the best linear colour transformation. Linear transformations have been proposed in the literature but independently. The aim of this paper is thus to establish a common mathematical background to all these methods. Also, this paper proposes a novel transformation, which is derived from the Monge-Kantorovicth theory of mass transportation. The proposed solution is optimal in the sense that it minimises the amount of changes in the picture colours. It favourably compares theoretically and experimentally with other techniques for various images and under various colour spaces.

Abstract-Image sequence restoration has been steadily gaining in importance with the increasing prevalence of visual digital media. The demand for content increases the pressure on archives to automate their restoration activities for preservation of the cultural heritage that they hold. There are many defects that affect archived visual material and one central issue is that of Dirt and Sparkle, or "Blotches." Research in archive restoration has been conducted for more than a decade and this paper places that material in context to highlight the advances made during that time. The paper also presents a new and simpler Bayesian framework that achieves joint processing of noise, missing data, and occlusion.