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Abstract

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.

Reflections in image sequences consist of several layers superimposed over each other. This phenomenon causes many image processing techniques to fail as they assume the presence of only one layer at each examined site e.g. motion estimation and object recognition. This work presents an automated technique for detecting reflections in image sequences by analyzing motion trajectories of feature points. It models reflection as regions containing two different layers moving over each other. We present a strong detector based on combining a set of weak detectors. We use novel priors, generate sparse and dense detection maps and our results show high detection rate with rejection to pathological motion and occlusion.

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This paper presents recent work on a new framework for non-blind document bleed-through removal. The framework includes image preprocessing to remove local intensity variations, pixel region classification based on a segmentation of the joint recto-verso intensity histogram and connected component analysis on the subsequent image labelling. Finally restoration of the degraded regions is performed using exemplar-based image inpainting. The proposed method is evaluated visually and numerically on a freely available database of 25 scanned manuscript image pairs with ground truth, and is shown to outperform recent non-blind bleed-through removal techniques.

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